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Full text of "Updating the geographic practice cost index : the physician work GPCI : final report"

REPORT DOCUMENTATION 

PAGE. 



PREAS SIGNED NTI^ 
PB94-161080 



^JpSatrnfthe Geographic Practice Cost-Index: The 
Physician Work GPCI 



May"l994 



7. Authortt) 



!• fsrfsrmiris; OrcMstntton ftsst. No. 



Gregory r P^mo riphra A. Dayhoff 



9. H w lfiii in g O i pi H aM wi Nam* and Addr — 

Health Economics Research, Inc 
300 Fifth Avenue 
6th Floor 

Waltham, MA 02154 



ia Prolfrt/T— fc/Worfc Unit No, 



U. ComwctC O or OrarttfO) No. 

(o 500-89-0050.5 
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IT. Spomsorfnc OrgswUsOon Nwm and Addra ss 

Health Care Financing Administration 

ORD/OR/DPES/NISB 

2-B-14 Oak Meadows Bldg, 

a^9^ SecxuciXy BuLud , Bal Hmnrp. r MP 31207 



1995, 1996 Final 



14. 



See also PB94-161072; PB94-161098; and PB94-161106. This study supports the Notice of Proposed 
Rule Making (BPD-789-P) "Medicare Program Refinements to the Physician Fee Schedule 
Geographic Factor Values and Other Changes", forthcoming in the Federal Register in June 1994. 



IK. Abstract (Umrfc 200 words) 

The Omnibus Budget Reconciliation Act (OBRA) of 1989 reformed Medicare payment for physician services 
as of January 1, 1992. On that date, Medicare began paying physicians on the basis of a national Medicare 
Fee Schedule (MFS) instead of usual, customary, and reasonable charges. Payments to physicians are now 
calculated from national uniform relative values and various conversion factors adjusted for justifiable 
geographic differences in physicians' cost of practice using a geographic adjustment factor termed the 
Medicare Geographic Practice Cost Index (GPCI). Subsequently, OBRA 1990 required that the GPCI be 
reviewed periodically and revised as necessary. The subject study supported HCFA's review of the original 
GPCIs; assessed the Medicare quarter work GPCI in effect from 1992-1994; and recommended a number 
of alternatives that might be used to update the quarter work GPCI. Study recommendations were to use: 
(1) 1990 Census data instead of 1980 data, (2) median earnings for professionals of all education levels (with 
a 1-digit occupation mix correction), (3) county-specific earnings where available in large metropolitan areas, 
and (4) relative-value weighing instead of population weighing. The report describes the Census data and 
methodology for calculating alternative physician work GPCIs. Tables include but are not limited to 
comparisons of ~ alternative (1995/1996) updated work GPCIs to the current 1992-1994 GPCIs (full 
variation and quarter variation); proposed physician work GPCI to GPCIs based on earnings of physicians 
and non-manufacturing workers; 1996 work GPCIs weighted by relative value units and by population; and 
proposed 1995 and 1996 GPCIs (reseated for budget neutrality)to the 1992-1994 GPCL. 

17. Documorrt AfioJysis s. Osocriptora 



b. Idswt l ftor s /Opow-Cwdsd Tsrms . T 

Medicare physician Dayment ; Georgraohic Practice Cost-Index; G^Ci; 
Laspeyres input price index; Medicare Fee Schedule; Geographic Adjustment 
Factor 



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Ss.ANSt-Z39.ir Sss ImttvctJora on ffsvsrss OPTIONAL fWH 272 (4-77) 

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UPDATING THE GEOGRAPHIC PRACTICE 
COST INDEX: THE PHYSICIAN 
WORK GPCI 



Final Report 



Submitted by : 

Gregory C Pcpe, MS. 
Debra A. Dayhoff, Ph-D. 



August 27, 1993 
(Revised May 1994) 



This report was prepared under Health Care Financing Administration Contract No. 500-89-0050 to Health Economics Research, 
Inc. The assistance of our HCFA Project Officer, Sherry Terrell, PhD., is gratefully acknowledged The statements contained in 
this report are solely those of the authors and do not necessarily reflect the views or policies of the Health Care Financing 
Administration. The contractor (or grantee) assumes responsibility for the accuracy and completeness of the information 
contained in this report. 




TABLE OF CONTENTS 



PAGE 

EXECUTIVE SUMMARY 

1.0 INTRODUCTION -1- 

1.1 Background on the Physician GPCI -1- 

1.2 Criticisms of the Physician Work GPCI -2- 

1.3 Overview of the Report -2- 

2.0 DATA FOR THE PHYSICIAN WORK GPCI -3- 

2.1 Description of Census Data -3- 

2.2 Sample Sizes in the Census Data . . . -4- 

3.0 METHODS FOR CALCULATING ALTERNATIVE PHYSICIAN WORK 

GPCIs -6- 

3.1 Occupation Shares for Professional Specialty Occupations -8- 

3.1.1 Computation of Shares -8- 

3.1.2 Updated Shares -8- 

3.2 Methods Used to Calculate Alternative Physicians Work GPCIs -10- 

3.2.1 Create County File of Wages for Occupation Categories -10- 

3.2.2 Calculate County Index -12- 

3.2.3 Translate County Index to Medicare Payment Localities -13- 

3.2.4 Quarter Variation Work GPCI -13- 

4.0 ALTERNATIVE PHYSICIAN WORK GPCIs -13- 

4.1 All Education Sample -14- 

4.2 Advanced Degree Sample -14- 

4.3 County Specific Earnings in CMSAs -20- 

4.4 Two Digit Occupations -22- 

4.5 Mean Earnings Instead of Median Earnings -25- 

5.0 COMPARISON OF PHYSICIAN WORK GPCI TO ALTERNATIVE 

EARNINGS INDEXES -28- 

5.1 Conceptual Issues in the Choice of Physician Work GPCI -28- 

5.2 Comparison of Relative Professional Earnings to Relative Earnings of 

All Non-manufacturing Workers -30- 

5.3 Comparison of Relative Professional Earnings to Relative Physician 
Earnings -33- 

5.3.1 Census Physician Earnings Data -33- 

5.3.2 Results -34- 

6.0 FINAL PAYMENT PHYSICIAN WORK GPCIs -40- 

REFERENCES 

APPENDIX 1 ALTERNATIVE UPDATED PHYSICIAN WORK GPCIs COMPARED TO 
1992 (CURRENT) GPCI (Full Variation) 

APPENDIX 2 ALTERNATIVE UPDATED PHYSICIAN WORK GPCIs COMPARED TO 
1992 (CURRENT) GPCI (Quarter Variation) 



TABLE OF CONTENTS (continued) 



APPENDIX 3 



APPENDIX 4 



COMPARISON OF PHYSICIAN WORK GPCI TO EARNINGS OF 
PHYSICIANS AND NON-MANUFACTURING WORKERS (Full Variation) 

DIFFERENCE BETWEEN 1996 PHYSICIAN WORK GPCI WEIGHTED BY 
RELATIVE VALUE UNITS (RVUs) AND POPULATION, BY MEDICARE 
PAYMENT LOCALITY, RANKED IN DESCENDING ORDER OF 
DIFFERENCE 



APPENDIX 5 
APPENDIX 6 



FINAL PHYSICIAN WORK GPCIs BY MEDICARE PAYMENT LOCALITY 

DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN WORK GPCI 
AND 1992 PHYSICIAN WORK GPCI, BY MEDICARE PAYMENT 
LOCALITY, RANKED IN DESCENDING ORDER OF DIFFERENCE 



1 



TABLE OF TABLES 



PAGE 



TABLE 2-1 DISTRIBUTION OF 1990 CENSUS PROFESSIONAL SPECIALTY 
OCCUPATION SAMPLE SIZES BY MEDICARE PAYMENT 
LOCALITY -5- 

TABLE 2-2 DISTRIBUTION OF 1990 CENSUS PROFESSIONAL SPECIALTY 
OCCUPATION SAMPLE SIZES BY MEDICARE PAYMENT 
LOCALITY, ADVANCED DEGREE SAMPLE -7- 

TABLE 3-1 OCCUPATIONAL SHARES FOR THE PHYSICIAN WORK GPCI -9- 

TABLE 3-2 TWO DIGIT OCCUPATIONAL SHARES FOR THE PHYSICIAN 

WORK GPCI -11- 



TABLE 4-1 LOCALITIES EXPERIENCING LARGE CHANGES IN THE 

PHYSICIAN WORK GPCI AS A RESULT OF UPDATING WITH 
1990 CENSUS DATA 



-15- 



TABLE 4-2 LOCALITIES EXPERIENCING LARGE CHANGES IN THE 

PHYSICIAN WORK GPCI AS A RESULT OF USING EARNINGS OF 
PROFESSIONALS WITH ADVANCED DEGREES 



-18- 



TABLE 4-3 LOCALITIES EXPERIENCING THE LARGEST CHANGES IN THE 
PHYSICIAN WORK GPCI AS A RESULT OF USING 
COUNTY-SPECIFIC EARNINGS IN CMSAs 



-21- 



TABLE 4-4 LOCALITIES EXPERIENCING THE LARGEST CHANGES IN THE 
PHYSICIAN WORK GPCI AS A RESULT OF USING 2-DIGIT 
PROFESSIONAL OCCUPATIONS -23- 

TABLE 4-5 LOCALITIES EXPERIENCING LARGE CHANGES IN THE 

PHYSICIAN WORK GPCI AS A RESULT OF USING MEAN RATHER 
THAN MEDIAN EARNINGS -26- 



TABLE 5-1 LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE 
PHYSICIAN WORK GPCI AND WAGES OF ALL 
NONMANUFACTURING WORKERS 



-31- 



TABLE 5-2 LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE 
PHYSICIAN WORK GPCI AND HOURLY EARNINGS OF 
PHYSICIANS 



-35- 



TABLE 5-3 LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE 
PHYSICIAN WORK GPCI AND HOURLY EARNINGS OF 
PHYSICIANS IN PHYSICIANS' OFFICES 



-37- 



UPDATING THE GEOGRAPHIC PRACTICE 
COST INDEX: THE PHYSICIAN WORK GPCI 



EXECUTIVE SUMMARY 



The Geographic Practice Cost Index (GPCT) is an input price index used to adjust 
Medicare Fee Schedule payments for geographic variations in physicians' practice costs. The 
GPCI has three components: physician work, practice expense, and malpractice insurance. 
The 1992 physician work GPCI is an index of professional earnings computed from 1980 
Census data. Congress specified that only one-quarter of the variation in the cost of 
physician work be reflected in Medicare payments. We refer to this payment GPCI as the 
"quarter work GPCI", and the professional earnings index as the "full work GPCI". This 
report presents alternative updated work GPCIs calculated with 1990 Census earnings data. 

We considered five alternatives for updating the physician work GPCI with 1990 
Census professional earnings. These alternatives were: 

(1) Earnings for professionals of all education levels; 

(2) Earnings for professionals with advanced degrees only; 

(3) County-specific earnings for counties in large metropolitan areas; 

(4) 2-digit professional occupations ather than 1-digit professional occupations; 
and 

(5) Mean hourly earnings rather than median hourly earnings. 

We recommended that median earnings for professionals of all education levels, with a 1- 
digit occupation mix correction, should be employed in the work GPCI. Although attractive 
in theory, Census sample sizes of professionals with advanced degrees, or classified into 2- 
digit occupations, are very small in the less populous Medicare payment localities. The 
advanced degree education or 2-digit occupation mix refinements could introduce 
undesirable random error into the GPCI in these localities. In addition, we prefer median 
earnings to mean earnings because medians are less sensitive to the extreme "tails" of the 
earnings distribution. 

Using earnings of professionals of all education levels represents a change in 
methodology from the current work GPCI, which was computed from 1980 Census earnings 
of professionals with 5 or more years of college education. That sample is no longer 
available with the 1990 Census. Using all education levels rather than only the most highly 



gpci\physrpt\text 



E-l 

6 



educated results in only small changes in the quarter work GPCI in virtually all payment 
localities. 

A further change we recommended in the updated (1996) work GPCI is weighting by 
work relative value units (RVUs) rather than by population. The reasons for weighting by 
RVUs are twofold. First, RVUs (which are available by county of location of service) provide 
a better indication of where services are provided. The GPCI should be weighted to reflect 
input prices at these locations. Second, weighting by RVUs guarantees that total Medicare 
Fee Schedule payments for physician work will not change if the payment localities are 
revised (e.g., if a state with multiple payment localities becomes a single statewide locality). 
Appendix 4 shows the (minor) differences between the 1996 RVU and population weighted 
quarter work GPCIs. 

We recommended that in large metropolitan areas, county-specific earnings should 
replace metropolitan-area-wide earnings. County earnings better account for input price 
variation within large metropolitan areas. Payment localities composed of central city 
regions (e.g., Manhattan, San Francisco) benefit from this change, whereas localities including 
suburban ring counties lose, although the changes are not large (see Table 4-3). We believe 
that this change is appropriate because input prices are in fact higher in central city areas. 
We did not utilize county-specific earnings for all counties because sample sizes of 
professionals in most counties are insufficient to accurately measure earnings. 

The Health Care Financing Administration concurred with our recommendations for 
the updated work GPCI. The 1996 work GPCI is based on 1990 Census median hourly 
earnings of professional workers of all education levels, county-specific earnings data in large 
metropolitan areas, a one-digit occupation mix correction, and weighting by work RVUs. 
The differences from the 1992 work GPCI are the use of 1990 rather than 1980 Census data, 
all education levels rather than 5 or more years of college, county-specific earnings in large 
urban areas rather than metropolitan area earnings, weighting by RVUs rather than 
population, the classification of all workers by place of work rather than some by place of 



gpci\ physrpt\ text 



E-2 

It 



residence, 1 and the use of the Office of Management and Budget's June 1993 metropolitan 
area redefinitions. 2 

The final GPCIs by Medicare payment locality are shown in Appendix 5. That table 
begins with the 1996 full work GPCI, then shows the 1996 quarter work GPCI. HCFA 
actuaries developed a multiplicative factor (1.00073) that was used to ensure that total 
Medicare Fee Schedule payments for physician work are not affected by the updated work 
GPCI. The 1996 rescaled quarter work GPCI reflects this factor. This rescaled GPCI will be 
used for payment beginning in 1996. The 1995 rescaled quarter work GPCI is a simple 
average of the 1996 rescaled quarter work GPCI and the 1992 quarter work GPCI. The 1995 
rescaled quarter work GPCI will be used for payment in 1995 only. 

Appendix 6 ranks the Medicare payment localities by descending difference between 
the 1996 rescaled quarter work GPCI and the 1992 quarter work GPCI. The changes in the 
quarter work GPCI index values from updating (1996 versus 1992 GPCI) are small. No 
Medicare payment locality gains or loses as much as 5 percent. The quarter work GPCI 
comprises about half of the overall Medicare Fee Schedule Geographic Adjustment Factor 
(GAF). Therefore, Medicare fees for a typical service will change by less than 2.5 percent in 
any area due to updating the work GPCI. Moreover, Congress has mandated a two-year 
transition from the 1992 to the 1996 GPCIs. The change in the 1995 transitional work GPCI is 
only half as large as the already small difference between the 1996 and 1992 GPCIs. 



'In the 1992 work GPCI, for sample size reasons, place of work classification was only used in large metropolitan areas. In 
other areas, workers were classified by their place of residence. 

2 Although the GPCI is eventually calculated for Medicare payment localities, the Census Bureau tabulates the underlying 
earnings data for metropolitan areas and the nonmetropolitan portions of states before it is crosswalked to localities (see 
Chapter 3). 



gpci\physrpt\ text 



E-3 
1 



1.0 INTRODUCTION 



1.1 Background on the Physician GPCI 

The Geographic Practice Cost Index (GPCI) is used in adjusting Medicare Fee 
Schedule payments for regional variations in physicians' cost of practice. The GPCI is an 
input price index that measures the relative cost of a standard market basket of physician 
practice inputs across areas. The Laspeyres index number form chosen for the GPCI requires 
two basic components: national shares of each practice input in total practice expenses and 
relative price terms for each area. GPCI index values for each area are calculated by 
weighting the relative price terms by the national expense shares to form an average relative 
price for practice inputs in each area. 

Relative prices by area are determined for four practice inputs: physician time, 
nonphysician employee wages, malpractice insurance premiums, and office rent. Geographic 
variation in the price of medical equipment, medical supplies, and miscellaneous is felt to be 
minimal. Congress specified that the GPCI be divided into three components for the 
Medicare Fee Schedule: the physician work GPCI, the practice expense GPCI, and the 
malpractice GPCI. Congress further mandated that the work GPCI reflect one-quarter of the 
variation in the cost of physician time. Zuckerman, Welch, and Pope (1987) argued that an 
index of professional earnings is an appropriate measure of relative physician time costs. 
Thus, the physician work GPCI is calculated as: 

GPCI(w) = 1 + (PROFESSIONAL WAGE INDEX - 1)/4. 

The index of professional wages used in the 1992 physician work GPCI was derived 
from a special tabulation of 1980 Census data. The Census classification "professional 
specialty occupations" was used to define six professional occupations (health diagnosing 
occupations such as physicians were excluded). Indexes of median hourly earnings were 
derived for each one and combined according to each occupation's share of the total 
professional specialty wage bill. Earnings were used only from individuals with 5 or more 
years of college education. The earnings index for metropolitan areas and the 
nonmetropolitan parts of states was then translated to Medicare payment localities. 



gpci\physrpt\text 



-1- 



1.2 Criticisms of the Physician Work GPCJ 



Several criticisms have been made of the physician work GPCI. First, the age of the 
1980 Census data currently used in the GPCI has been criticized. Second, the use of 
metropolitan-wide median earnings in the GPCI, including for some possibly higher-cost 
central city localities such as Manhattan, New York, has been questioned (PPRC, 1992). 
Finally, some have wondered why an index of professional earnings is used in the GPCI 
rather than a measure of physician income itself. 

We address each of these criticisms in this report. We present updated versions of 
the physician work GPCI using 1990 Census data and compare them to the 1992 1980- 
Census-based GPCI. One variant of the updated GPCI uses county-specific earnings in large 
metropolitan areas to capture higher costs in central-city localities. The choice of an index of 
professional earnings versus one of physician earnings is largely a conceptual issue that was 
discussed in Zuckerman, Welch, and Pope (1987). We review the reasons why professional 
earnings are preferable for the GPCI in Chapter 5. Also, in Chapter 5, we compare 1990 
Census physician hourly earnings to professional hourly earnings. 

1.3 Overview of the Report 

In the next chapter, we describe the 1990 Census data analyzed in this report, 
including sample sizes for alternative occupation/education categories by Medicare locality. 
Chapter 3 presents 1990 Census occupational wage bill shares for professional specialty 
occupations and compares them to their 1980 Census counterparts. Chapter 4 presents and 
evaluates alternative physician work GPCIs computed from the 1990 Census data. 
Differences are evaluated by focussing on Medicare payment localities with large changes in 
GPCIs between variants. Both full and quarter variation work GPCIs are presented. Chapter 
5 compares the work GPCIs derived from professional earnings to indexes of 
nonmanufacturing earnings and physician earnings. 

Chapter 6 discusses the final GPCIs that the Health Care Financing Administration 
has decided to use for payment. Appendices 1 and 2 contain tables comparing alternatives 
for updating the physician work GPCI to the 1992 work GPCI. Appendix 1 makes the 
comparison for the full work GPCI and Appendix 2 for the quarter work GPCI. Appendix 3 
compares the updated GPCI to alternative earnings series, of all non-manufacturing workers, 
and of physicians. Appendix 4 compares RVU- and population-weighted updated work 



gpci\ physrpt\ text 



-2- 

0\ 



GPCIs. Appendix 5 contains a table of the final work GPCIs to be used in the Medicare Fee 
Schedule, and Appendix 6 examines the difference between the 1996 and 1992 quarter work 
GPCIs. The index values in Appendices 1, 2, and 3 are not directly comparable to the index 
values in Appendices 5 and 6. The GPCIs in Appendices 1, 2, and 3 are population- 
weighted, whereas the GPCIs in Appendices 5 and 6 are work-RVU-weighted. Also, 
Appendices 4, 5, and 6 reflect the Office of Management and Budget's June 1993 update of 
metropolitan area definitions, which are not reflected in Appendices 1, 2, and 3. 

ZO DATA FOR THE PHYSICIAN WORK GPCI 

Relative professional earnings are used to measure the relative cost of physician time 
in the work GPCI. Zuckerman, Welch, and Pope (1987) concluded that decennial Census 
data were the best available to measure professional earnings, and 1980 Census data are used 
in the current GPCI. Since that report, the Physician Payment Review Commission (PPRC, 
1991) and the U. S. General Accounting Office (GAO, 1993) have reviewed the GPCI and 
have concluded that the Census data is an appropriate choice for use in the physician work 
GPCI, although the 1980 Census data need to be updated. 

2.1 Description of Census Data 

As part of the decennial Census, questions on income, hours worked, place of work, 
and occupation are asked of roughly 20 percent of the United States population (the exact 
sampling rate varies by type of area). All Census data are self reported. Health Economics 
Research, Inc., contractor to the Health Care Financing Administration, obtained a special 
tabulation of 1990 Census earnings data by occupation and area from the U.S. Bureau of the 
Census. The sample is the experienced civilian labor force. Earnings are defined as the sum 
of wage or salary income and net income from self-employment. (Nonlabor income is 
excluded.) Both mean and median hourly earnings are tabulated, with workers classified by 
their place of work. For the 1980 Census, place of work was tabulated for only one-half of 
the full sample. In 1990, place of work is assigned for most workers, so the place of work 
sample is nearly as large as the place of residence sample. All 1990 Census data presented in 
this report classify workers by place of work. Earnings data were obtained for metropolitan 
areas, counties, states, and the nonmetropolitan portions of states. 



gpci\physrpt\text 



-3- 

10 



For certain occupations, earnings data were also obtained with the sample restricted 
to individuals possessing advanced degrees, defined as a master's degree, professional school 
degree (e.g., MBA, JD), or doctorate. The 1990 Census educational classifications, which are 
by highest degree earned, differ from the 1980 classification by years of schooling. Thus, it 
was not possible to obtain earnings exactly comparable to the 1980 data. For 1990, data is 
available for all-education and advanced degree samples, but not for 5 or more years of 
college. Earnings for some occupations are also available cross-classified by certain industry 
groupings, such as earnings of all non-manufacturing workers. 

2.2 Sample Sizes in the Census Data 

Table 2-1 presents the distribution by Medicare payment locality of 1990 Census 
sample sizes for each occupation group comprising the physician work GPCI, and for office 
based physicians. These sample sizes are for workers of all education levels. Census 
provides estimates of the total number of people in each occupation/ area cell. We have 
divided by 6 to (conservatively) account for the Census "long form" sampling rate of 15-20 
percent on earnings questions. The share (weight) of each occupation in the physician work 
GPCI is also indicated. 

Sample sizes for teachers are large, much greater than for any other one-digit 
occupation category. The smallest sample is 250, and earnings in three-fourths of all 
localities is based cn more than 2,000 teachers. Sample sizes ar^ much smaller for e our 
occupation groups: (1) engineers, (2) social scientists, social workers, and lawyers, (3) RNs 
and pharmacists, and (4) writers, artists and athletes. However, the smallest locality samples 
of 30 to 59 workers are not so small that we would expect median earnings to be ~ object to 
significant random sampling error. The sample of natural scientists and mathematicians is 
quite small in some localities, with an estimated miriimum sample of 10. However, the small 
sample is a problem for only a few localities; 95 percent of the localities have samples larger 
than 39 (the fifth percentile value), and 90 percent of the localities have samples larger than 
72 (the tenth percentile value). In addition, with an share of only 10.1 percent in the 
professional earnings index, random variation in the measured earnings of natural scientists 
and mathematicians does not have a major effect on the physicians' work GPCI. 

We also present the distribution of sample sizes for office based physicians. 
Physicians' earnings were excluded from the GPCI. However, in Chapter 5 we present 
comparisons of the professional earnings GPCI with an index of physicians' earnings 



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calculated from the Census data. Physician samples in some localities are quite small; the 
minimum is 5, the first percentile is 13, and the fifth percentile value is 21. These small 
samples are likely to result in a significant amount of random sampling error in reported 
physician earnings. 

The sample sizes of the two digit occupations are much more problematic, as shown 
in the bottom panel of Table 2-1. Six of the fourteen occupation categories have minimum 
sample sizes smaller than 10; three more have minimum samples of between 10 and 20 
workers. Six categories have fifth percentile values of Jess than 25,, indicating that the small 
sample sizes are not just a problem for one or two localities. While these occupations 
generally do not have large expenditure shares, small sample sizes for several occupations in 
the same locality could result in a large fraction of the GPC1 being based on the median 
wages of a very small number of workers. 

Table 2-2 presents sample sizes for the one-digit occupation categories from the 
advanced degree sample, which are also problematic in the least populous localities. Sample 
sizes for three of the occupation groups, (1) engineers, (2) natural scientists and 
mathematicians, and (3) writers, artists, and athletes are quite small in som. 1 localities. Each 
of these categories has a minimum sample size of one or two workers, and a tenth percentile 
value less than 20. Sample sizes for RNs and pharmacists are only slightly larger, with a 
minimum value of 8. 

We conclude that the Census sample sizes of one-digit occupations, all education 
levels, are sufficient to accurately measure median hourly earnings by Medicare payment 
locality. Earnings estimates for two-digit occupations and for the advanced degree sample 
are subject to significant random sampling error in the less populous localities, which makes 
their use in the physician work GPCI problematic. 

3.0 METHODS FOR CALCULATING ALTERNATIVE PHYSICIAN WORK GPOs 

The physician work GPCI has two components: (1) expenditure shares by 
professional specialty occupational category, and (2) geographic wage indexes for 
professional specialty occupational categories. Section 3.1 discusses determination of the 
occupation shares and compares the updated shares with those used in constructing the 
current GPCI. Section 3.2 discusses the methodology used for creating the alternative GPCIs 
based on indexes of professional earnings for Medicare payment localities. 



gpci\ physrpt\text 



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3.1 Occupation Shares for Professional Specialty Occupations 

3.1.1 Computation of Shares 

The 1992 physician work GPCI is composed of earnings indexes for six one-digit 
professional specialty occupations: (1) engineers, surveyors, and architects; (2) natural 
scientists and mathematicians; (3) social scientists, social workers, and lawyers; (4) teachers, 
counselors, and librarians; (5) RNs and pharmacists; and (6) writers, artists, and editors. The 
health diagnosing occupations category (which includes physicians) was intentionally omitted 
from the work GPCI. 

To determine updated occupational shares, the 1990 Census national hourly wage bill 
for each occupation was estimated by multiplying national median hourly earnings by 
number of workers, for each occupational category. The share for each occupation was then 
calculated as the occupation's wage bill as a share of the sum of the wage bills of all six 
professional specialty occupations used in the GPCI. Shares were calculated analogously for 
the all education and advanced degree samples, and for the 14 two-digit occupations. 

3.1.2 Updated Shares 

Table 3-1 presents the 1992 work GPCI occupation shares and the updated one digit 
occupation shares calculated for both the advanced degree sample and the all education 
sample. The advanced degree shares are similar to the current shares, which are based on 5 
or more years of college education. The all-education shares differ significantly from the 
shares of the more highly educated. 

Teachers, counselors, and librarians comprise the largest portion of the work GPCI. 
This group's shares are quite similar for the current GPCI (44 percent) and the advanced 
degree sample (42 percent), but substantially lower for the all education sample (31 percent). 
Teachers, counselors, and librarians contribute^ a lower percentage of total hours for 
professional specialty workers of all education levels than professional workers with 
advanced degrees, accounting for much of this difference in shares. This pattern is repeated 
for social scientists, social workers and lawyers; the shares for the 1992 GPCI (26 percent) and 
the advanced degree sample (29 percent) are quite similar while the share for the all 
education sample (15 percent) is much lower. In contrast, the share for RNs and pharmacists 
is over twice as large (16 percent) in the all education sample as in the advanced degree 



gpci\ physrpt\ text 



-8- 



TABLE 3-1 



OCCUPATIONAL SHARES FOR THE PHYSICIAN WORK GPCI 



Updated share 







Advanced 


All 




1992 


Degree 


Education 


Occupation Cateaorv 


GPCI Share 


Sample 


Sample 


Engineers, Surveyors, and Architects 


.140 


.109 


.187 


Natural Scientists and Mathematical 


.067 


.076 


.101 


Social Scientists, Social Workers, and Lawyers 


.259 


.294 


.154 


Teachers, Counselors, and Librarians 


.436 


.416 


.307 


RNs and Pharmacists 


.056 


.069 


.158 


Writers, Artists, and Editors 


.042 


.036 


.093 



NOTE: Shares are based on national median hourly earnings multiplied by number of workers. 
SOURCE: 1980 Census; 1990 Census Special Tabulation 



-9- 



gpci\physrpt\TAB3-1 . 



sample (7 percent) or the 1992 GPCI (6 percent). The relatively small number of RNs and 
pharmacists with advanced degrees (as defined by Census) accounts for this difference. 

Table 3-2 presents expenditure shares for two digit occupational categories (all 
education sample). The three largest shares are for non-postsecondary teachers (23 percent), 
engineers (18 percent), and registered nurses (11 percent). In contrast, the seven smallest 
shares (out of fourteen total) have a combined expenditure weight of less than 20 percent. 

3.2 Methods Used to Calculate Alternative Physicians Work GPCIs 
3.2.1 Create County File of Wages for Occupation Categories 

Census earnings data were obtained for metropolitan areas, counties, states, and the 
nonmetropolitan portions of states. Census uses several metropolitan area definitions. 
Metropolitan Statistical Area (MSA) is the most general term. Consolidated Metropolitan 
Statistical Areas (CMSA) are the largest, most populous MSAs. Primary Metropolitan 
Statistical Areas (PMSAs) are smaller metropolitan areas that are the constituents of CMSAs. 
In New England, MSAs are not county-based, so Census also defines New England County 
Metropolitan Areas (NECMAs). We began our analysis by creating two files of median 
earnings by occupation. The county is the unit of observation for both files, but, for most 
counties, earnings data is mapped onto counties from a higher geographic level (e.g., median 
metropolitan area earnings mapped onto all counties comprising the MSA). Counties cannot 
be used as the basis for creating the GPCI everywhere because sample sizes are too small to 
permit accurate estimation of earnings for each county. The two analytic files are: 

1. A county level file with metropolitan area and statewide nonmetropolitan 
Census earnings by occupation. Outside of New England, metropolitan areas 
were defined as either Primary Metropolitan Statistical Areas (PMSAs) within 
Consolidated Metropolitan Areas (CMSA), or Metropolitan Statistical Areas 
(MSAs) outside of CMSAs. In New England, metropolitan area was defined as 
New England County Metropolitan Areas (NECMAs) rather than MSAs. 
Nonmetropolitan areas were defined as all counties in a state not located in an 
MSA or NECMA. For example, every county in Wyoming is included as an 
observation on this file. Counties in the Casper MSA (the only metropolitan 
area in Wyoming) are all assigned the Casper MSA median earnings. All other 
counties in the state are assigned Wyoming nonmetropolitan median earnings. 

2. A county level file with county earnings for counties in CMSAs, metropolitan 
area earnings for counties in MSAs, but not in CMSAs, and statewide 
nonmetropolitan earnings for counties not in MSAs or CMSAs. Thus, this file 
is identical to that described in 1. above, except that PMSA earnings in CMSAs 



gpci\ physrpt\ text 



-lO- 
ll 



TABLE 3-2 

TWO DIGIT OCCUPATIONAL SHARES FOR THE PHYSICIAN WORK GPCI 



Occupation Cateqorv 


Share 


Engineers 


.181 


Mathematical and computer scientists 


.067 


Natural scientists 


.031 


Social scientists, and urban planners 


.028 


Social, recreation, and religious workers 


.050 


Lawyers and judges 


.103 


Teachers, post-secondary 


.049 


Teachers, except post-secondary 


.229 


Vocational counselors 


.013 


Librarians 


.009 


Registered nurses 


.114 


Pharmacists, therapists 


.039 


Writers, artists 


.062 


Editors, reporters 


.025 



NOTE: Shares are based on national median hourly earnings multiplied by number of workers. 
SOURCE: 1 990 Census Special Tabulation 



-11- 



gpci\physrpt\TAB3-2.XLS 



are replaced with county earnings. Census defines three CMSAs in New 
England— Boston, Hartford, and Providence—but they are not county based. 
Three NECMAs match the three New England CMSAs. For the purposes of 
creating this file, we treated the Boston, Massachusetts, Hartford, Connecticut, 
and Providence, Rhode Island NECMAs as CMSAs. That is, we used county 
earnings for counties within these three NECMAs. 3 

Both hies described above contain earnings for each occupation, and for all 
stratifications (i.e., industry, education). Two parallel files were then created for mean rather 
than median earnings. All files also contain county population from the 1990 Census. 

3.2.2 Calculate County Index 

County indices were created using two different county weights: population and 
work relative value units (RVUs). To maintain comparability with the 1992 work GPCI, and 
because RVUs by county were unavailable when the work was originally done, alternative 
work GPCIs were explored using population weights. All GPCIs presented prior to Chapter 
6, and in Appendices 1, 2, and 3 are population weighted. The final payment GPCIs 
(Chapter 6 and Appendix 5), however, are weighted by RVUs for the reasons discussed in 
Chapter 6. The choice of population or RVU weights has only a very small effect on the 
index values (Appendix 4). The same methods, described now, were used with either 
weight. 

The population or RVU-weighted national average of the wages assigned tc counties 
was taken for each professional specialty occupation group. Although they are included on 
the file, Puerto Rico, the Virgin Islands, and Guam were excluded from this national average, 
as they were in calculating the current (1980 Census based) physicians' work GPCI .* A 
county-level index was computed for each occupation by dividing each county's wage by the 
population or RVU-weighted national average. The overall county-level physician work 
index was then calculated by multiplying these occupation-specific indexes by the occupation 
cost shares from Table 3-1 or Table 3-2 and summing. 



'In their June 1993 revision of metropolitan area definitions, Census/OMB defined only one CMSA in New England 
Boston Thus, for the final payment GPCIs (discussed in Chapter 6 and shown in Appendix 5), only the Boston NECMA was 
considered to be equivalent to a CMSA. Several towns in Connecticut, mostly in Fairfield county, are part of the New York City 
CMSA and also part of the New Haven-Bridgeport^tamford-Danbury-Waterbury, Connecticut NECMA. We considered 
Fairfield county to be part of the NECMA, not the CMSA for purposes of computing the final payment GPCIs. 

4 Whether or not Puerto Rico and other outlying areas are included in the national average does not affect the final 
payment index values because the values are rescaled to maintain budget neutrality (Le., the same aggregate work payments to 
physicians). 

gpci\ physrpt\ text -12- 



A 



3.2.3 Translate County Index to Medicare Payment Localities 

Finally, the county index was translated to Medicare payment localities using a 
crosswalk. Locality indexes are population or RVU-weighted averages of their constituent 
county indexes. If a locality includes only part of a county, that county index is included in 
the locality average weighted by the population or RVUs of the county part. If a locality is 
entirely within one county, it is assigned that county's index. 

3.2.4 Quarter Variation Work GPCI 

Congress specified that the physician work GPCI should reflect one-quarter of the 
variation in physician work. We used the following formula to translate the "full variation" 
index of professional specialty earnings into the one-quarter variation index: 

One-quarter variation GPCI = 1 + (Full Variation GPCI - l)/4. 

4.0 ALTERNATIVE PHYSICIAN WORK GPCIs 

This chapter presents five alternatives for updating the 1992 physician work GPCI, 
which is based on 1980 Census median hourly earnings of professionals. These alternatives 
are: 

(1) 1990 Census earnings for professionals of all education levels; 

(2) 1990 Census earnings for professionals with advanced degrees only; 

(3) 1990 Census data with county-specific earnings for counties in Consolidated 
Metropolitan Statistical Areas (CMSAs); 

(4) 1990 Census earnings for 2-digit professional occupations rather than 1-digit 
professional occupations; and 

(5) 1990 Census mean hourly earnings rather than median hourly earnings. 

The next four subsections—Sections 4.1 through 4.5— describe the rationale for each of 
these alternatives, and then identify which Medicare payment localities' physician work 
GPCIs change the most as a result of changing the method of computation. We present both 
the full and the quarter work GPCIs. The quarter work index would be the basis for 
payment. The full work index shows the actual variation in professional earnings. 



gpci\ physrpt\ text 



-13- 
10 



4.1 All Education Sample 



We begin our comparisons of alternative GPCIs by comparing the 1992 (1980 Census) 
GPCI to an updated GPCI based on the 1990 Census all education sample of professionals. 
Changes in full work locality GPCIs from updating are significant. Table 4-1 shows that 69 
localities, or nearly 30 percent of all localities, experience a change of more than six 
percentage points in the full work GPCI from updating to the 1990 data. Twenty five 
localities experience a gain of more than six percentage points. The three localities 
experiencing the largest gains are Northwest and North Central Connecticut (17.5 percentage 
points), South Central Connecticut (14.4 percentage points), and Eastern Connecticut (13.8 
percentage points). Seven of the 9 localities experiencing the largest increases are located in 
New England. 

Forty-four localities, or approximately 20 percent of all localities, experience a loss of 
more than six percentage points in the full variation physicians' work GPCI when updated 
with 1990 data. Two localities, Odessa, Texas, and Alaska lose more than 13 percentage 
points. Ten localities, five of which are in Illinois, lose between 10 and 13 percentage points. 
The changes in the quarter work (payment) GPCI are much smaller. No payment locality 
gains or loses as much as 5 percentage points. 

4.2 Advanced Degree Sample 

The 1992 physician work GPCI was constructed using the earnings of professional 
workers with at least 5 years of college. The purpose of the education adjustment was to 
restrict professionals to those whose preferences are expected to be similar to physicians'. 
Since physicians have extensive graduate training, it might be desirable to base the work 
GPCI on a group with similar education attainment. However, with the 1980 Census data, a 
work GPCI based on the advanced education sample was found to differ little from the all- 
education sample GPCI (Welch, Zuckerman, and Pope, 1989). 

We acquired 1990 Census data by occupation for two education samples: workers 
with all education levels, and workers with an advanced degree (masters, doctorate, J.D., 
etc.). Thus, neither sample is exactly comparable to the 5 years of college sample used 
previously to construct the GPCI with 1980 Census data (that sample is no longer available). 
The advanced degree sample contains workers who are more similar to physicians in 
educational attainment, but its smaller sample sizes raises concerns that earnings may reflect 

gpci\ phy srpt\ text ^ ^ 



TABLE 4-1 



LOCALITIES EXPERIENCING LARGE CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF UPDATING WITH 1990 CENSUS DATA 8 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


Updated 


1992 




Updated 


A AAA 

1992 




Locality Name 


Locality 


GPCI 


GPCI 


Chanqe 


GPCI 


GPCI 


Chanqe 


Larqest Increases 
















NW AND N. CENTRAL CONN. 


10230/01 


1.046 


1.002 


0.044 


1.183 


1.008 


0.175 


SOUTH CENTRAL CONN. 


10230/03 


1.054 


1.018 


0.036 


1.216 


1.072 


0.144 


EASTERN CONN. 


10230/04 


1.034 


0.999 


0.035 


1.134 


0.996 


0.138 


ATLANTA, GA 


01040/01 


1.008 


0.975 


0.033 


1.032 


0.900 


0.132 


RICHMOND+CHARLOTTESVL, VA 


10490/01 


1.005 


0.975 


0.030 


1.021 


0.900 


0.121 


MASSACHUSETTS URBAN 


00700/01 


1.032 


1.002 


0.030 


1.128 


1.008 


0.120 


VERMONT 


00780/50 


0.970 


0.942 


0.028 


0.881 


0.768 


0.113 


NEW HAMPSHIRE 


00780/40 


0.990 


0.962 


0.028 


0.958 


0.848 


0.110 


MASS.SUBURBS/RURAL(CITIES) 


00700/02 


1.021 


0.997 


0.024 


1.083 


0.988 


0.095 


SAN FRANCISCO, CA 


00542/05 


1.059 


1.038 


0.021 


1.237 


1.152 


0.085 


SAN MATEO, CA 


00542/06 


1.059 


1.038 


0.021 


1.237 


1.152 


0.085 


TIDEWATER+N. VA COUNTIES 


10490/02 


1.009 


0.989 


0.020 


1.038 


0.956 


0.082 


QUEENS, NY 


14330/04 


1.079 


1.059 


0.020 


1.316 


1.236 


0.080 


MANHATTAN, NY 


00803/01 


1.079 


1.059 


0.020 


1.316 


1.236 


0.080 


AUSTIN, TX 


00900/31 


0.989 


0.969 


0.020 


0.955 


0.876 


0.079 






fl Q75 

U. «7 f J 


956 


0.019 


0.902 


0.824 


0.078 


NYC SUBURBS/LONG I., NY 


00803/02 


1.079 


1.060 


0.019 


1.316 


1.240 


0.076 


SMALL GA CITIES 02 


01040/02 


0.981 


0.962 


0.019 


0.924 


0.848 


0.076 


CENTRAL MAINE 


21200/02 


0.961 


0.942 


0.019 


0.844 


0.768 


0.076 


NORTHERN NEW JERSEY 


00860/01 


1.058 


1.040 


0.018 


1.233 


1.160 


0.073 


W. CENTR (LAKE PLAINS), OH 


16360/00 


0.987 


0.969 


0.018 


0.947 


0.876 


0.071 


MC ALLEN, TX 


00900/19 


0.961 


0.945 


0.016 


0.845 


0.780 


O.Ooj 


NORTHERN MAINE 


21200/01 


0.963 


0.947 


0.016 


0.852 


0.788 


0.064 


SANTA CLARA, CA 


00542/09 


1.064 


1.048 


0.016 


1.256 


1.192 


0.064 


MARION + SURR. CNTYS., OH 


16360/00 


0.987 


0.971 


0.016 


0.947 


0.884 


0.063 


Larqest Decreases 
















ODESSA, TX 


00900/13 


0.960 


1.008 


-0.048 


0.842 


1.032 


-0.190 


ALASKA 


01020/01 


1.060 


1.106 


-0.046 


1.242 


1.424 


-0.182 


MINNESOTA STATEWIDE 


00720/00 


0.967 


0.999 


-0.032 


0.867 


0.996 


-0.129 


BRAZORIA, TX 


00900/09 


0.993 


1.025 


-0.032 


0.972 


1.100 


-0.128 


PEORIA, IL 


00621/05 


0.979 


1.009 


-0.030 


0.914 


1.036 


-0.122 


SHREVEPORT, LA 


00528/02 


0.974 


1.003 


-0.029 


0.895 


1.012 


-0.117 


ALEXANDRIA, LA 


00528/07 


0.958 


0.985 


-0.027 


0.833 


0.940 


-0.107 


SOUTHERN IL 


00621/14 


0.948 


0.974 


-0.026 


0.790 


0.896 


-0.106 


QUINCY, IL 


00621/07 


0.948 


0.974 


-0.026 


0.790 


0.896 


-0.106 



-is- r{y 



TABLE 4-1 (continued) 



LOCALITIES EXPERIENCING LARGE CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF UPDATING WITH 1990 CENSUS DATA" 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


Undated 


1992 




Undated 


1992 




Locality Name 


Local Hv 

laVVHlIlT 


GPCI 


GPCI 


Channp 


GPCI 


GPCI 


Chanae 


SOUTHEAST, IL 


00621/13 

wUUi. 1 / Is/ 


Q4P, 


ft Q7A 
U.y 1 4 


ft ftOR 

-U.UZO 


ft 7QA 

U. /9U 


0.896 


A A AC 

-0.106 


NORTHWEST, IL 


00621/01 


u.yto 


u.y / 4 


ft noc 
-U.U20 


0.790 


<"\ o ac 

0.896 


a a r\r* 

-0.106 


NORTHWEST IOWA 


00640/06 


U.y*tO 


u.yoy 


-U.U2D 


0. /74 


a o 7^ 

0.876 


A A A A 

-0.102 


BROWNSVILLE, TX 






u.you 


ft noc 
-U.UZ5 


A oil 

0.822 


A AAA 

0.920 


A Ari ft 

-0.098 


CLEVELAND, OH 


16360/00 


D Qft7 
u.yo / 


I .U I I 


ft ftO/4 

-U.U24 


A C\ A~7 

0.947 


a r\ A A 

1 .044 


A A A"7 

-0.097 


ROCK ISLAND, IL 


00621/04 


n Q72 


u.yyo 


ft mi 
-U.U2.5 


A Q O O 

U.888 


0.980 


A AAA 

-0.092 


DECATUR, IL 


00621/1 1 


U . cf JO 


u.yo i 


ft DOO 


a ooo 
U.833 


a a o a 
0.924 


A A A A 

-0.091 


W + SE WA (EXCL SEATTLE) 


00932/01 




I ,uuo 


ft ftOI 

-U.UZo 


n a>i a 

u.y4i 


a nil 
1 .032 


a r\(\A 

-0.091 


EL PASO, TX 


00900/14 


Q73 


n qq^ 
u.yyo 


ft mo 


ft OAA 

U.89U 


a non 
0.980 


A AAA 

-0.090 


DE KALB, IL 


00621/03 


0.956 


97R 


-ft 09? 


ft ft07 


ft Q1 O 

u.y i z 


ft ftRO 

-u.uoy 


S.CEN. IA(EXCL DES MOINES) 


00640/04 


Q40 




ft mo 
-U.UZZ 


ft 7C1 
U. /Ol 


U.848 


A AQ"7 

-0.087 


SOUTH DAKOTA 


00820/02 


930 


9o1 

U.3J I 


-U.UZ 1 


ft 71 ft 

U. / I o 


ft ftft/t 
U.0U4 


-U.UbO 


WYOMING 


00825/21 


Q67 


u.yoo 


ft ftOI 

-U.UZ I 


ft QC7 

U.8D / 


0.952 


A AO C 

-0.085 


MONROE, LA 


00528/05 


U.9 JO 


n Q7Q 

u.y / y 


ft mi 
-U.U21 


A QOO 

0.832 


0.916 


A A O A 

-0.084 


NORTH DAKOTA 


00820/01 


94^ 


U.auj 


n non 
-U.UZU 


ft 7Dft 

U. /8U 


n oca 
U.8bU 


A AOA 

-0.080 


MIAMI, FL 


00590/04 


1 01 5 


1 034 


n m Q 
-u.u i y 


1 ftft 1 


I 1 3D 


ft ft7C 

-U.U / 5 


CHICAGO, IL 


00621/16 

\J\J\JA- 1/ IU 


I .U<iO 


1 r\A a 


a a -i q 
-U.U i 8 


4 A AO 

1.102 


1.176 


-0.074 


MONTANA 


WW / J l/U I 


u.y*ty 


n OC7 

u.yo / 


-0.01 8 


0.795 


0.868 


-0.073 


LAS VEGAS, ET AL(CITIES),NV 


01 PQ0/01 


I . U l 


1 .UoO 


n a a o 
-U.U1 8 


1 .071 


4 AAA 

1 .144 


-0.073 


NORTH CENTRAL IOWA 


00640/03 


953 


971 


.ft ft1 Q 

-u.u I o 


ft ft1 o 


ft ftft/t 


ft ft70 

-U.U/2 


MIDLAND, TX 


00900/23 


1.005 


1.023 


-0.018 


1.021 


1.092 


-0.071 


WICHITA FALLS, TX 


00900/34 


0.951 


0.969 


-0.018 


0.806 


0.876 


-0.070 


SPRINGFIELD, OH 


16360/00 


0.987 


1.004 


-0.017 


0.947 


1.016 


-0.069 


MERCED/SURR. CNTYS, CA 


00542/10 


1.001 


1.018 


-0.017 


1.003 


1.072 


-0.069 


SOUTHWEST IOWA 


00640/07 


0.951 


0.968 


-0.017 


0.804 


0.872 


-0.068 


WHEELING, WV 


16510/17 


0.958 


0.975 


-0.017 


0.832 


0.900 


-0.068 


STOCKTON/SURR. CNTYS, CA 


00542/08 


1.002 


1.019 


-0.017 


1.008 


1.076 


-0.068 


DETROIT, Ml 


00710/01 


1.042 


1.059 


-0.017 


1.168 


1.236 


-0.068 


SE IOWA (INCL IOWA CITY) 


00640/01 


0.959 


0.976 


-0.017 


0.837 


0.904 


-0.067 


DES MOINES(POLK/WARREN),IA 


00640/05 


0.980 


0.997 


-0.017 


0.921 


0.988 


-0.067 


NE RURAL CA 


00542/02 


0.98^ 


1.001 


-0.017 


0.937 


1.004 


-0.067 


UTAH 


00910/09 


0.976 


0.993 


-0.017 


0.906 


0.972 


-0.066 


KANKAKEE, IL 


00621/06 


0.956 


0.972 


-0.016 


0.825 


0.888 


-0.063 


MONTEREY/SANTA CRUZ, CA 


00542/12 


1.008 


1.023 


-0.015 


1.030 


1.092 


-0.062 


WACO, TX 


00900/22 


0.966 


0.981 


-0.015 


0.863 


0.924 


-0.061 



•More than a six percentage point gain or loss in the full-work GPCI. 

NOTE: 1992 GPCI is 5 or more years of college, 1-digit occupations, MSA earnings, 1980 Census data. 
Updated GPCI is all education levels, 1-digit occupations, MSA earnings, 1990 Census data. 

-16- r\'b 



random error rather than true wage differentials. The sample size analysis in Chapter 2 
showed that the advanced degree sample is quite small for a sizeable portion of Medicare 
localities for some professional occupations. 

To determine the effects of the advanced degree restriction,, Table 4-2 shows localities 
with a large difference between the all education GPCI (option 1 above) and the advanced 
degree GPCI. As expected, the changes are the greatest in less populous localities, which are 
the most sensitive to random sampling variation. For example, the Grayson, Texas locality 
experiences the largest change from using the advanced degree rather than the all education 
sample, 41 percentage points in the full work GPCI and 10 percentage points in the quarter 
work GPCI. Grayson's advanced degree GPCI is implausible; it exceeds the GPCIs of San 
Francisco, Oakland, urban Massachusetts, and nearly equals the Los Angeles GPCI. 

Table 4-2 shows that five other localities experiences gains of 8 or more percentage 
points in the full variation GPCI. Three of these— Victoria, Texas, McAllen, Texas, Wichita 
Falls, Texas— are also very small localities. Another twenty two localities experience gains of 
between four and eight percentage points. Sixteen localities lose more than 4 percentage 
points in the full variation GPCI as a result of using the advanced degree sample. The 
largest losses are 6 percentage points for Oakland/ Berkeley, California and for Hawaii. 
Other large losers include both areas that would be expected to have relatively high 
proportions of individuals with advanced degrees (Marin and San Francisco, California, 
Urban Massachusetts, Charlottesville, Virginia) and small city localities (Waco and Temple, 
Texas). Changes in the quarter work GPCI are minor. With the exception of Grayson and 
Victoria Texas, no locality's quarter work GPCI differs by m ->re than 2.5 percentage points 
between the advanced degree and all education samples. 

In sum, we favor the all-education GPCI because the small advanced degree samples 
seem to create an undesirable amount of randomness in the work GPCI. This represents a 
change in methods from the current GPCI, but the 5 or more years of college sample is not 
available from the 1990 Census, so some change ir. methods is necessary. Moreover, 
previous analyses with 1980 Census data showed that the 5 years of college and all education 
work GPCIs did not differ significantly, and with the 1990 Census data, the advanced degree 
and all education quarter work GPCIs show only minor differences. 



gpci\ physrpt\ text -17- 




TABLE 4-2 



LOCALITIES EXPERIENCING LARGE CHANGES IN THE PHYSICIAN WORK GPC) AS A 
RESULT OF USING EARNINGS OF PROFESSIONALS WITH ADVANCED DEGREES" 



QUARTER-VARIATION 



FULL-VARIATION 



Locality Name 



Advanced AM 
Carrier/ Degree Education 

Locality Sample Sample Change 



Advanced All 
Degree Education 
Sample Sample Change 



Largest Increases 



GRAYSON, TX 


00900/16 


1.061 


0.959 


0.102 


1 .243 


0.836 


ft Aft"! 


VICTORIA, TX 


00900/32 


1.029 


0.983 


0.046 


1.117 


ft a 

0.934 


ft A QA 
U.l 04 


ANAHEIM/SANTA ANA, CA 


02050/26 


1.061 


1.038 


0.023 


1.243 


A A c r\ 

1.150 


ft ftftl 

0.09.3 


MC ALLEN, TX 


00900/19 


0.984 


0.961 


0.023 


0.936 


0.845 


ft ftflO 

0.092 


WICHITA FALLS, TX 


00900/34 


0.972 


0.951 


0.021 


0.889 


ft ft ft /!?> 

0.806 


ft nQQ 


KINGS/TULARE, CA 


00542/13 


1.008 


0.988 


0.020 


1.032 


0.952 


0.080 


NE RURAL, CA 


00542/02 


1.002 


0.984 


0.018 


1.008 


0.937 


ft ft "7 4 

0.071 


TEXARKANA, TX 


00900/08 


0.972 


0.955 


0.016 


0.887 


0.821 


ft ftftC 

O.Oob 


EL PASO, TX 


00900/14 


0.989 


0.973 


0.016 


0.956 


0.890 


ft r\c c 

0.065 


LUBBOCK, TX 


00900/21 


0.970 


0.955 


0.016 


0.881 


u.ol o 


n nft^ 

U.UDO 


MERCED/SURR.CNTYS, CA 


00542/10 


1.016 


1.001 


0.016 


1.065 


1.003 


0.062 


FRESNO/MADERA, CA 


00542/1 1 


1.015 


1.000 


0.014 


1.059 


1.002 


0.057 


LOS ANGELES (1ST OF 8) 


02050/18 


1.071 


1.057 


0.014 


1.283 


1.227 


0.056 


LOS ANGELES (2ND OF 8) 


02050/19 


1.071 


1.057 


0.014 


1.283 


1.227 


0.056 


LOS ANGELES (3RD OF 8) 


02050/20 


1.071 


1.057 


0.014 


1.283 


1.227 


0.056 


LOS ANGELES (4TH OF 8) 


02050/21 


1.071 


1.057 


0.014 


1.283 


1.227 


0.056 


LOS ANGELES (5TH OF 8) 


02050/22 


1.071 


1.057 


0.014 


1.283 


1.227 


0.056 


LOS ANGELES (6TH OF 8) 


02050/23 


1.071 


1.057 


0.014 


,.283 


1.227 


0.056 


LOS ANGELES (7TH OF 8) 


02050/24 


1.071 


1.057 


0.014 


1.283 


1.227 


0.056 


LOS ANGELES (8TH OF 8) 


02050/25 


1.071 


1.057 


0.014 


1.283 


1.227 


0.056 


PUERTO RICO 


00973/20 


0.895 


0.881 


0.014 


0.578 


0.523 


0.055 


WAUSAU (N CNTRL), Wl 


00951/36 


0.976 


J. 963 


0.014 


0.905 


0.850 


0.055 


ALEXANDRIA, LA 


00528/07 


0.972 


0.958 


0.014 


0.888 


0.833 


0.055 


VIRGIN ISLANDS 


00973/50 


0.980 


0.966 


0.013 


0.918 


0.865 


0.053 


SAN DIEGO/IMPERIAL, CA 


02050/28 


1.031 


1.018 


0.013 


1.123 


1.070 


0.052 


KANKAKEE, IL 


00621/06 


0.969 


0.956 


0.013 


0.876 


0.825 


0.052 


LAFAYETTE, LA 


00528/06 


0.986 


0.975 


0.012 


0.946 


0.898 


0.047 


VENTURA, CA 


02050/17 


1.040 


1.029 


0.012 


1.161 


1.114 


0.047 


LONGVIEW, TX 


00900/17 


0.985 


0.974 


0.011 


0.941 


0.897 


0.045 


RENO, ET AL. (CITIES), NV 


01290/02 


1.008 


0.997 


0.011 


1.032 


0.988 


0.044 


PHOENIX, AZ 


01030/01 


1.016 


1.005 


0.011 


1.064 


1.020 


0.044 


STOCKTON/SURR. CNTYS , CA 


00542/08 


1.013 


1.002 


0.011 


1.051 


1.008 


0.043 



-18- 

4 



TABLE 4-2 (continued) 



LOCALITIES EXPERIENCING LARGE CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF USING EARNINGS OF PROFESSIONALS WITH ADVANCED DEGREES' 



QUARTER-VARIATION FULL-VARIATION 



Advanced All Advanced All 

Carrier/ Degree Education Degree Education 

Locality Name Locality Sample Sample Change Sample Sample Change 



Largest Decreases 



OAKLAND/BERKLEY, CA 


00542/07 


1.027 


1.042 


-0.015 


1.108 


1.168 


-0.060 


HAWAII 


01120/01 


0.981 


0.996 


-0.015 


0.924 


0.984 


-0.060 


MARIN/NAPA/SOLANO, CA 


00542/03 


1.012 


1.026 


-0.015 


1.046 


1.104 


-0.058 


VERMONT 


00780/50 


0.956 


0.970 


-0.014 


0.825 


0.881 


-0.056 


URBAN MASS 


00700/01 


1.019 


1.032 


-0.013 


1.075 


1.128 


-0.053 


CENTRAL MAINE 


21200/02 


0.949 


0.961 


-0.012 


0.794 


0.844 


-0.049 


WHEELING, WV 


16510/17 


0.946 


0.958 


-0.012 


0.783 


0.832 


-0.049 


PEORIA, IL 


00621/05 


0.967 


0.979 


-0.012 


0.868 


0.914 


-0.047 


SAN FRANCISCO, CA 


00542/05 


1.048 


1.059 


-0.011 


1.191 


1.237 


-0.046 


SAN MATEO, CA 


00542/06 


1.048 


1.059 


-0.011 


1.191 


1.237 


-0.046 


RICHMOND & C-VILLE, VA 


10490/01 


0.994 


1.005 


-0.011 


0.977 


1.021 


-0.043 


NORTHERN MAINE 


21200/01 


0.952 


0.963 


-0.011 


0.809 


0.852 


-0.043 


WACO, TX 


00900/22 


0.955 


0.966 


-0.011 


0.821 


0.863 


-0.042 


TEMPLE, TX 


00900/06 


0.957 


0.968 


-0.010 


0.829 


0.871 


-0.042 


REST OF PA 


00865/04 


0.963 


0.974 


-0.010 


0.853 


0.895 


-0.042 


MASS SUBURBS/RURAL CITIES 


00700/02 


1.011 


1.021 


-0.010 


1.042 


1.083 


-0.041 



■More than a four percentage point gain or loss in the full-work GPCI. 
NOTE: 1 -digit occupations, MSA earnings. 



-19- 



4.3 County Specific Earnings in CMSAs 

The 1992 physician work GPCI, as well as the two updated options presented above, 
utilize metropolitan-wide median hourly earnings for each county within an MSA. In 
smaller metropolitan areas this is probably not a significant shortcoming because earnings are 
likely to be reasonably homogeneous across the entire metropolitan area. However, 
significant earnings variation may exist within larger metropolitan areas. The third 
alternative physician work GPCI establishes county-specific earnings for counties within the 
larger metropolitan areas, defined as Consolidated Metropolitan Statistical Areas, or CMSAs. 

The most useful comparison for this option is the 1990 update without county-specific 
earnings (option 1 for updating). This comparison isolates the effects of using county-specific 
earnings. The largest differences by payment locality between the 1990 updated GPCI and 
the 1990 update with county-specific earnings displayed in Table 4-3. 

As expected, localities comprised of core cities of large metropolitan areas are the 
gainers, while suburban ring localities are the losers. Manhattan, New York (6.9 percentage 
points), the Philadelphia/ Pittsburgh, Pennsylvania Medical Schools locality (4.2 percentage 
points), and San Francisco, California (4.2 percentage points) are the largest winners, and the 
only localities to experience gains of more than four percentage points in their full work 
GPCIs. Denton, Texas experiences the largest loss, a fourteen percentage point decrease in 
the full variation physician GPCI, as a result of using the county-specific earnings. This 
locality is part of suburban Dallas, and we believe the change reflects actual earnir g 
differences, not random measurement error. The Queens, New York, Rockford, Illinois, and 
New York City Suburbs localities also experience losses greater than four percentage points 
in their full work GPCIs. 

Overall, the changes with county-specific CMSA earnings are not large. Only 25 
localities experience changes greater than one percentage point in their full work GPCIs. 
Only six localities' quarter work GPCIs change by more than one percentage point. 
Nevertheless, we believe that use of county-specific earnings within CMSAs is a justified 
means of reflecting higher central city input prices. We believe the method of computing the 
work GPCI should be modified to reflect these higher costs. 



gpci\ physrpt\ text - 2 - 




TABLE 4-3 



LOCALITIES EXPERIENCING THE LARGEST CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF USING COUNTY-SPECIFIC EARNINGS IN CMSAs" 



QUARTER-VARIATION FULL-VARIATION 







CMSA/ 






CMSA/ 








Carrier/ 


County 


MSA 




County 


MSA 




Locality Name 


Locality 


(?PCI 


Data 


Chanoe 


QPPI 


Data 


Ghana 


Larqest Increases 
















MAMUATTAM KIV 

MAIN MA 1 IAN, NY 


UUoUoVU I 


1 noc 

i .uyo 


1 C17Q 

i .u ( y 


n n 1 7 


1 IOC 


1 n R 

1 . 1 D 


n nRQ 
u.uoy 


DUII 1 v/ditt Men cun C/UHCDC DA 


UUOOD/U I 


1 nio 


1 .UZ 1 


U.U1 \ 


1 1 07 


1 nR^ 

1 .UOD 


U.U4Z 


CAM CDAMPICPH A 

oAlN rKAlNL/loUU, OA 


UUD4Z/UD 


1 n7n 

1 .U / U 


i .UDy 


U.U 1 u 


1 07R 


1 9"57 
I .Zj / 


U.U4Z 


DAI t AC TY 


nnonn/1 1 
uuyuu/ 1 i 


1 m 

I .U I o 


1 nn/i 

I .UU4 


U.UUO 


I .UD I 


1 m 7 

I .U I 1 


n 


MM WAI Ik'PP \A/I 
IVMLVvr\Ur\CC, Vvl 


nno^i ir\A 
uuy d i /U't 


1 nn? 
I .uuz 


n QQR 
u.yyo 


U.UUD 


I .UUO 


n qr^ 
u.yoD 


U.UZO 


nFTROIT Ml 
UC 1 r\<^M 1 , IVII 


uu / I U/U I 


I . Uh / 




U.UUD 


1 1 RR 

I . I oo 


1 1 RR 


n n?n 

U.UZU 


HOI ICTOM TY 
nuuO 1 WIN, 1 A 


nnann/1 r 
uuyuu/ 1 o 


I .uzz 


1 m 7 

I .U I / 


U.UUD 


1 orr 
I .uoo 


1 ("IRQ 

i ,uoy 


n m q 
u.u i y 


PHIPAOO II 


nnftoi i\ r 

UUOZ 1 / 1 


i .uzy 


I .UZO 


u.uu4 


1 117 


1 1 no 

I . I uz 


U.U I D 


nFI AUVARP 

UtLnvVnr\C 


UUD / U/U I 


I XJl. I 


1 m r 

I .U I 


U.UUo 


1 nRA 


1 071 
I .u / I 


U.U 1 o 


FORT WORTH TY 




n QRQ 
u.yoy 


n qrr 

u .sou 


U.UUo 


n q^r 


n Q4R 


U.U 1^ 


















nFMTOM TY 


uuyuu/i /. 


n orq 

u.yoy 


1 .UU4 


-U.U jD 


r> R7R 

U.o io 


1 <v 7 
1 .U i / 


n 1 41 

-U. I 4 I 


QUEENS, NY 


14330/04 


1.059 


1.079 


-0.020 


1.237 


1.316 


-0.079 


ROCKFORD, IL 


00621/02 


0.981 


0.997 


-0.016 


0.924 


0.987 


-0.063 


NYC SUBURBS/LONG 1., NY 


00803/02 


1.066 


1.079 


-0.013 


1.263 


1.316 


-0.054 


MARIN/NAPA/SOLANO, CA 


00542/03 


1.016 


1.026 


-C.010 


1.066 


1.104 


-0.039 


bAN MATEO, CA 


00542/06 


1.050 


1.059 


-0.009 


1.200 


1.237 


-0.037 


NORTHEAST RURAL TX 


00900/02 


0.964 


0.972 


-0.008 


0.855 


0.888 


-0.034 


MILWAUKEE SURBURBS (SE), Wl 


00951/46 


0.989 


0.997 


-0.008 


0.955 


0.988 


-0.033 


SOUTHEAST RURAL TX 


00900/03 


0.969 


0.975 


-0.006 


0.874 


0.899 


-0.025 


SM CITIES (CITY LIMITS) KY 


00660/02 


0.963 


0.969 


-0.006 


0.854 


0.877 


-0.023 


SOUTH & E. SHORE MD 


00690/03 


1.006 


1.010 


-0.004 


1.023 


1.040 


-0.017 


SUBURBAN CHICAGO, IL 


00621/15 


1.007 


1.011 


-0.004 


1.030 


1.046 


-0.016 


LG PA CITIES 


00865/02 


1.007 


1.010 


-0.003 


1.027 


1.040 


-0.013 


MIDDLE NJ 


00860/02 


1.034 


1.038 


-0.003 


1.138 


1.150 


-0.012 


MICHIGAN, NOT DETROIT 


00710/02 


0.996 


0.999 


-0.003 


0.983 


0.995 


-0.012 



•More than a one percentage point gain or loss in the full-variation GPCI. 
NOTE: All education sample of professionals, 1 -digit occupations. 



-21- 



4.4 Two Digit Occupations 



The 1992 physicians' work GPCI, as well as the alternatives presented above, all rely 
on wages for six one-digit professional specialty occupations. The advantage of the one-digit 
occupations is that the broader occupational categories have larger sample sizes than the 
more narrowly defined two-digit occupations, reducing random noise in the GPCI. The 
disadvantage of the one-digit categories is that some include occupations with clissimilar 
earnings levels. For example, a one-digit occupation category includes social scientists, urban 
planners, social, recreation and religious workers, and lawyers and judges. Lawyers and 
judges earn much more than social, recreation, and religious workers, on average. If some 
localities have a higher proportion of lawyers, the GPCI will be inappropriately high there. 
The two digit occupations permit a more refined occupation mix adjustment, separating, for 
example, lawyers and judges from social, recreation, and religious workers. See Table 2-1 for 
a list of the six one-digit and 14 two-digit occupations and their sample size distributions by 
Medicare payment locality. 

Table 4-4 shows localities experiencing the largest changes in the physicians' work 
GPCI as a result of using two digit occupations rather than one digit occupation categories. 
As might be expected, the localities with the largest gains from the use of two-digit 
occupations are predominantly small city or rural, while those experiencing large loses are 
highly urbanized. Washington, DC and suburbs, San Francisco, Miami, the New York City 
area, and Los Angeles are among the largest losers. The largest gainers include small 
localities in Texas and Illinois. Presumably the larger urban areas have higher-earning 
occupation mixes, while the small city/ rural localities have lower-earning occupation mixes. 
Thus, a more refined two-digit occupation adjustment benefits the less populous localities at 
the expense of the highly urbanized areas. 

The finer two-digit occupation mix adjustment makes sense. Nevertheless, it may 
introduce more random variation into the work GPCI in addition to improving the 
occupation mix adjustment. Table 2-1 shows that some localities have very small two-digit 
sample sizes. Also, the changes in the work GPCI from using two-digit occupations are not 
very large. Only two localities gain more than 5 percentage points, and only three lose more 
than 5 percentage points in the full variation GPCI. No locality gains or loses as much as 
two percentage points in its quarter work GPCI. We prefer to continue to use the more 
stable one-digit earnings in the work GPCI. 



gpd\physrpt\text 



-22- 



TABLE 4-4 



LOCALITIES EXPERIENCING THE LARGEST CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF USING 2-DIGIT PROFESSIONAL OCCUPATIONS" 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


Two-Digit 


One-Digit 




Two-Digit 


One-Digit 




Locality Name 


Locality 


GPCI 


GPCI 


Chanae 


GPCI 


GPCI 


Chanae 


Larqest Increases 
















VICTORIA, TX 


00900/32 


1.002 


0.983 


0.019 


1.009 


0.934 


0.076 


MONROE, LA 


00528/05 


0.972 


0.958 


0.014 


0.887 


0.832 


0.054 


WICHITA FALLS, TX 


00900/34 


0.963 


0.951 


0.012 


0.854 


0.806 


0.048 


TEXARKANA, TX 


00900/08 


0.967 


0.955 


0.012 


0.867 


0.821 


0.046 


SAN ANGELO, TX 


00900/30 


0.959 


0.948 


0.011 


0.835 


0.791 


0.043 


DECATUR, IL 


00621/11 


0.968 


0.958 


0.010 


0.872 


0.833 


0.039 


QUINCY, IL 


00621/07 


0.956 


0.948 


0.009 


0.826 


0.790 


0.035 


SOUTHERN IL 


00621/14 


0.956 


0.948 


0.009 


0.826 


0.790 


0.035 


NORTHWEST, IL 


00621/01 


0.956 


0.948 


0.009 


0.826 


0.790 


0.035 


SOUTHEAST IL 


00621/13 


0.956 


0.948 


0.009 


0.826 


0.790 


0.035 


LONGVIEW, TX 


00900/17 


0.982 


0.974 


0.008 


0.928 


0.897 


0.032 


REST OF LA 


00528/50 


0.970 


0.962 


0.008 


0.878 


0.848 


0.030 


DE KALB, IL 


00621/03 


0.963 


0.956 


0.007 


0.853 


0.823 


0.030 


REST OF IN 


00630/03 


0.980 


0.972 


0.007 


0.919 


0.890 


0.030 


REST OF PA 


00865/04 


0.981 


0.974 


0.007 


0.923 


0.895 


0.029 


GRAYSON, TX 


00900/16 


0.966 


0.959 


0.007 


0.865 


0.836 


0.028 


KINGS/TULARE, CA 


00542/13 


0.995 


0.988 


0.007 


0.981 


0.952 


0.028 


MERCED/SURR.CNTYS, CA 


00542/10 


1.008 


1.001 


0.007 


1.031 


1.003 


0.028 


PEORIA, IL 


00621/05 


0.986 


0.979 


0.007 


0.942 


0.914 


0.028 


ST JOSEPH, MO 


00740/01 


0.959 


0.953 


0.007 


0.837 


0.811 


0.026 


S CNTRL IA (EXCL. DES MOINES) 


00640/04 


0.947 


0.940 


0.006 


0.787 


0.761 


0.C26 


REST OF FLORIDA 


00590/01 


0.973 


0.966 


0.006 


0.892 


0.866 


0.U26 


MC ALLEN, TX 


00900/19 


0.968 


0.961 


0.006 


0.870 


0.845 


0.026 


BROWNSVILLE, TX 


00900/10 


0.962 


0.955 


0.006 


0.846 


0.822 


0.025 


CORPUS CHRISTI, TX 


00900/24 


0.989 


0.983 


0.006 


0.956 


0.931 


0.025 


FORT WORTH, TX 


00900/28 


0.993 


0.986 


0.006 


0.970 


0.946 


0.025 


REST OF MO 


11260/03 


0.949 


0.943 


0.006 


0.796 


0.772 


0.023 


ALEXANDRIA, LA 


00528/07 


0.964 


0.958 


0.006 


0.856 


0.833 


0.023 


REST OF AL 


00510/06 


0.975 


0.970 


0.006 


0.902 


0.879 


0.023 


SW OR CITIES (CITY LIMITS) 


01380/12 


0.973 


0.967 


0.006 


0.892 


0.870 


0.023 


NORTHEAST IOWA 


00640/02 


0.964 


0.958 


0.006 


0.854 


0.832 


0.022 


NORTH IDAHO 


05130/12 


0.961 


0.955 


0.006 


0.843 


0.821 


0.022 


YUMA, AZ 


01030/08 


0.990 


0.984 


0.006 


0.960 


0.938 


0.022 


OSHKOSH (E CNTRL), Wl 


00951/60 


0.977 


0.972 


0.005 


0.909 


0.888 


0.021 


NORTH CENTRAL, AL 


00510/02 


0.976 


0.971 


0.005 


0.903 


0.882 


0.021 


SOTH DAKOTA 


00820/02 


0.935 


0.930 


0.005 


0.739 


0.718 


0.021 



-23- 



TABLE 4-4 



LOCALITIES EXPERIENCING THE LARGEST CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF USING 2-DIGIT PROFESSIONAL OCCUPATIONS 8 



QUARTER-VARIATION FULL-VARIATION 





well 1 WtWI 








Two-Digit One-Digit 




I nfuilrfv Mam a 
i_uuaiiiv w at lie 


L.UUCIIIIV 


ft DO 
UrUI 




onange 


GPCI 


GPCI 




LdlQcSI UcClcaSeS 
















DC +MnA/A IRI IRR<^ 


00580/01 


1 .042 


1 .057 


-0.015 


1 1R7 

I . I D / 


1 228 


-0.061 




00542/05 


1 .045 


1.059 


-0.014 


1 1 7Q 

I.I 19 


1 2^7 

I ./LO i 


-0.057 


<^AN MATFO PA 

WAIN 1 V 1 C W , w 


00542/06 


1 .045 


1.059 


-0.014 


1 1 7Q 

i . i / y 


1 2^7 
I .£.0 1 


-0.057 


CAMTA P| ADA CA 


00542/09 


1 .056 


1.064 


-0.008 


1.224 


1.256 


-0.032 


MIAMI Fl 


00590/04 


1 .008 


1.015 


-0.007 


1.033 


1.061 


-0.027 


NEW ORLEANS, LA 


00528/01 


0.992 


0.998 


-0.007 


0.966 


0.993 


-0.026 


AUSTIN, TX 


00900/31 


0.982 


0.989 


-0.006 


0.929 


0.955 


-0.026 


NYC SUBURBS/LONG 1., NY 


00803/02 


1.073 


1.079 


-0.006 


1.292 


1.316 


-0.025 


NW ANDN. CNTRL CT 


10230/01 


1.040 


1.046 


-0.006 


1.160 


1.183 


-0.024 


MIDLAND, TX 


00900/23 


0.999 


1.005 


-0.006 


0.997 


1.021 


-0.023 


MANHATTAN, NY 


00803/01 


1.073 


1.079 


-0.006 


1.293 


1.316 


-0.023 


QUEENS, NY 


14330/04 


1.073 


1.079 


-0.006 


1.293 


1.316 


-0.023 


SW CT 


10230/02 


1.060 


1.066 


-0.006 


1.241 


1.263 


-0.022 


LOS ANGELES (1ST OF 8) 


02050/18 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 


LOS ANGELES (2ND OF 8) 


02050/19 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 


LOS ANGELES (3RD OF 8) 


02050/20 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 


LOS ANGELES (4TH OF 8) 


02050/21 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 


LOS ANGELES (5TH OF 8) 


02050/22 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 


LOS ANGELES (6TH OF 8) 


02050/23 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 


LOS ANGELES (7TH OF 8) 


02050/24 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 


LOS ANGELES (8TH OF 8) 


02050/25 


1.051 


1.057 


-0.005 


1.205 


1.227 


-0.021 



"More than a two percentage point gain or loss in the full-variation GPCI. 
NOTE: All education sample of professionals, MSA earnings. 



-24- 



4.5 Mean Earnings Instead of Median Earnings 

Our first four GPCI alternatives are based on median hourly earnings, as is the 1992 
physician work GPCI computed from 1980 Census data. An important advantage of median 
earnings is that they are less sensitive to the extremes of the earnings distribution. Hence, 
medians are less likely to be affected by anomalous, "outlier" reported wages than are means. 
Especially with small samples, as occurs in some of the less populous localities, median 
earnings are likely to be more stable and have a lower standard error than mean earnings. In 
addition, medians are a better measure of earnings of the "typical" employee than are means. 
Mean earnings could reflect, for example, a skewed distribution at upper earnings levels due 
to a few high-earnings individuals. Unusual extremes of the earnings distribution should not 
be reflected in the GPCI. 

The one advantage of mean earnings is that they can be consistently aggregated to 
different geographic levels. For example, state-wide mean earnings are a weighted average 
of individual payment locality mean earnings. Median earnings do not have this property: 
the state-wide median is not a weighted average of the individual locality medians. Mean 
earnings can therefore be scaled consistently from the county level to the state level. This 
could be an advantage if the geographic area for physician payment are modified, as 
recommended, for example, by the Physician Payment Review Commission (PPRC, 1992). 

Table 4-5 presents localities experiencing changes of more than three percentage 
points in the full-variation physician work GPCI as a result of using mean rather than 
median earnings. Eleven localities experience gains of more than 10 percentage points in the 
full work GPCI from using means: Victoria, Texas, Grayson, Texas, Manhattan, New York, 
and the eight Los Angeles, California localities. Another 19 localities gain between three and 
10 percentage points; many of these are large city localities. The large gains of the two small 
Texas localities are probably an artifact of small sample sizes, for example, a few high- 
earning professionals. Manhattan and Los Angeles probably gain because of the presence of 
a larger proportion of very high earning individuals in some of the professional occupations. 
For example, a large number of very high earning corporate lawyers work in New York, 
while Los Angeles is the workplace of a disproportionately large number of very high 
earning artists and entertainers. The other large cities that gain probably similarly have a 
disproportionate number of very high-earning professionals. 

Twenty five localities experience losses of more than 3 percentage points in the full 
work GPCI as a result of using mean earnings data. The losers are predominantly small 



gpci\physrpt\text 



-25- 



FABLE 4-5 




LOCALITIES EXPERIENCING LARGE CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF USING MEAN RATHER THAN MEDIAN EARNINGS" 




QUARTER-VARIATION 


FULL-VARIATION 


Carrier/ Means Median 
Locality Name Locality GPCI GPCI Chanae 


Means Median 
GPCI GPCI Chanae 


Laraest Increases 



VICTORIA, TX 


00900/32 


1.044 


0.984 


0.060 


1.176 


0.937 


0.239 


GRAYSON, TX 


00900/16 


1.001 


0.960 


0.041 


1 .003 


A a A A 

0.839 


0.164 


MANHATTAN, NY 


00803/01 


1.128 


1.097 


0.031 


1.511 


1.386 


A A A C 

0.125 


LOS ANGELES (1ST OF 8) 


02050/18 


1.084 


1.058 


0.027 


1.338 


1.232 


A A AA 

0.106 


LOS ANGELES (2ND OF 8) 


02050/19 


1.084 


1.058 


0.027 


1.338 


A A A A 

1.232 


0.106 


LOS ANGELES (3RD OF 8) 


02050/20 


4 no/ 


1 .000 


U.uz / 


l .000 


I .i-Oi- 


106 


LOS ANGELES (4TH OF 8) 


02050/21 


1.084 


1.058 


0.027 


1.338 


1.232 


0.106 


LOS ANGELES (5TH OF 8) 


02050/22 


1.084 


1.058 


0.027 


1.338 


1.232 


0.106 


LOS ANGELES (6TH OF 8) 


02050/23 


1.084 


1.058 


0.027 


1.338 


1.232 


0.106 


LOS ANGELES (7TH OF 8) 


02050/24 


1.084 


1.058 


0.027 


1.338 


1.232 


0.106 


LOS ANGELES (8TH OF 8) 


02050/25 


1.084 


1.058 


0.027 


1.338 


1.232 


0.106 


VIRGIN ISLANDS 


00973/50 


0.985 


0.967 


0.018 


0.941 


0.868 


0.072 


MIAMI, FL 


00590/04 


1.033 


1.016 


0.017 


1.132 


1.065 


0.068 


HOUSTON, TX 


00900/18 


1.037 


1.022 


0.015 


1.148 


1.088 


0.060 


ANAHEIM/SANTA ANA, CA 


02050/26 


1.053 


1.039 


0.015 


1.214 


1.155 


0.059 


SAN FRANCISCO, CA 


00542/05 


1.081 


1.070 


0.011 


1.324 


1.279 


0.045 


CHICAGO, IL 


00621/16 


1.040 


1.029 


0.011 


1.161 


1.117 


0.044 


SANTA BARBERA, CA 


02050/16 


1.018 


1.007 


0.011 


1.071 


1.028 


0.043 


FORT LAUDERDALE, FL 


00590/03 


1.010 


1.000 


0.010 


1.040 


0.998 


0.041 


DALLAS, TX 


00900/1 1 


1.023 


1.013 


0.010 


1.091 


1.052 


0.039 


AUSTIN, TX 


00900/31 


0.999 


0.990 


0.010 


0.998 


0.959 


0.038 


LAREDO, TX 


00900/33 


0.967 


0/958 


0.010 


0.868 


0.830 


0.038 


BATON ROUGE, LA 


00528/03 


0.995 


0.985 


0.009 


0.978 


0.940 


0.038 


DELAWARE 


00570/01 


1.030 


1.021 


0.009 


1.119 


1.085 


0.035 


NEW ORLEANS, LA 


00528/01 


1.008 


0.999 


0.008 


1.031 


0.997 


0.034 


LAFAYETTE, LA 


00528/06 


0.984 


0.975 


0.008 


0.935 


0.902 


0.034 


PHILLY/PITT MED SHCLS/HOSPS, PA 


00865/01 


1.040 


1.032 


0.008 


1.161 


1.128 


0.033 


ORANGE, TX 


00900/25 


1.002 


0.994 


0.008 


1.008 


0.975 


0.033 


BEAUMONT, TX 


00900/20 


1.002 


0.994 


0.008 


1.008 


0.975 


0.033 


ATLANTA, GA 


01040/01 


1.017 


1.009 


0.008 


1.069 


1.036 


0.032 



-26- 



TABLE 4-5 (continued) 



LOCALITIES EXPERIENCING LARGE CHANGES IN THE PHYSICIAN WORK GPCI AS A 
RESULT OF USING MEAN RATHER THAN MEDIAN EARNINGS" 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


Means 


Median 




Means 


Median 




Locality Name 


Locality 


GPCI 


GPCI 


Chanae 


GPCI 


QPCI 


Chana< 


Laroest Dpcrpa^ps 
















TEMPLE TX 


00900/06 

W w WWW/ Ww 


0.950 


0.968 


-0.018 


0.802 


0.874 


-0.072 


YUMA AZ 


01030/08 

W 1 www/ ww 


969 

W . WW w 


985 


-0 016 

w . w I \J 


876 

w . \J 1 w 


941 

w . w*t 1 


-0 065 

W . WW w 


SW OR CITIES (CITY LIMITS} 


01380/12 

W 1 WwW/ 1 4> 


0.955 


0.968 


-0.01 3 


0.821 


0.873 


-0.052 


LACROSSE (W CNTRL' Wl 


00951/19 

W W W *J 1 / I w 


962 

W . W W Am. 


974 

w . W I " 


-0 01 ? 

w . w 1 u. 


848 


895 

w . ww w 


-0.047 


JANESVILLE (S CNTRL} Wl 


00951/54 


954 

W . W *J*T 


96^ 

W . W W w 


-0 01 1 

w . w 1 1 


816 


861 

W . WW 1 


-0.045 


QUEENS NY 


14330/04 

1 ~ www/ W^ 


1 048 

1 • W*T W 


1 059 

I . W W W 


-0 01 1 

W . W 1 1 


1 193 

1 . 1 w w 


1 238 

1 . ^ w w 


-0.045 


NORTHWEST Wl 


00951/12 


0.952 


0.963 


-0.01 1 


0.809 


0.853 


-0.044 


ALASKA 


01020/01 


1.051 


1.062 


-0.011 


1.202 


1.246 


-0.044 


OSHKOSH (E CNTRL), Wl 


00951/60 


0.962 


0.973 


-0.010 


0.849 


0.891 


-0.042 


CENTRAL Wl 


00951/13 


0.949 


0.960 


-0.010 


0.798 


0.839 


-0.041 


! SOUTHWEST Wl 


00951/14 


0.949 


0.960 


-0.010 


0.798 


0.839 


-0.041 


SAN BERNADINO/E.CTRL CNTYS CA 


00542/15 


1.007 


1.016 


-0.010 


1.027 


1.065 


-0.039 


DENTON, TX 


00900/12 


0.960 


0.969 


-0.009 


0.840 


0.877 


-0.037 


REST OF OREGON 


01380/99 


0.962 


0.971 


-0.009 


0.847 


0.884 


-0.036 


MADISON (DANE CNTY), Wl 


00951/15 


0.982 


0.991 


-0.009 


0.927 


0.963 


-0.036 


STOCKTON/SURR. CNTYS , CA 


00542/08 


0.994 


1.003 


-0.009 


0.976 


1.011 


-0.036 


REST OF IN 


00630/03 


0.964 


0.973 


-0.009 


0.856 


0.891 


-0.036 


MASS SUBURBS/RURAL CITIES 


00700/02 


1.009 


1.017 


-0.009 


1.035 


1.069 


-0.034 


MINNESOTA (BLUE SHIELD) 


00720/00 


0.959 


0.967 


-0.009 


0.836 


0.870 


-0.034 


AMARILLO, TX 


00900/26 


0.967 


0.976 


-0.008 


0.869 


0.903 


-0.034 


MILWAUKEE SURBURBS (SE). Wl 


00951/46 


0.981 


0.989 


-0.008 


0.923 


0.955 


-0.032 


NYC SUBURBS/LONG 1., NY 


00803/02 


1.058 


1 .066 


-0.008 


1.231 


1.263 


-0.032 


ST JOSEPH, MO 


00740/01 


0.945 


0.953 


-0.008 


0.782 


0.814 


-0.032 


BAKERSFIELD , CA 


00542/14 


1.017 


1.025 


-0.008 


1.066 


1.098 


-0.032 


W & SE WA (EXCL SEATTLE) 


00932/01 


0.977 


0.984 


-0.008 


0.907 


0.938 


-0.031 



'More than a three percentage point gain or loss in the full-variation GPCI. 

^OTE: Both GPCIs utilize earnings of 1 -digit occupations, all education sample, and county-specific data within CMSAs. 



city/rural localities. The largest losers are Temple, Texas (7.2 percentage points), Yuma, 
Arizona (6.5 percentage points), and Southwest Oregon Cities (5.2 percentage points). These 
less populous areas lack the high earning professionals present in larger cities and therefore 
have relatively lower mean earnings than median earnings. 

We prefer the medians-based index because of the lesser sensitivity of median 
earnings to the extremes of the earnings distribution. This reduces the susceptibility of the 
work GPCI to random fluctuations. The medians-based GPCI also measures typical earnings 
better than the means-based GPCI, which can be influenced by a small proportion of 
unusually high-earning professionals in an area. We believe that the GPCI should compare 
the earnings of similar workers in different areas. 

5.0 COMPARISON OF PHYSICIAN WORK GPCI TO ALTERNATIVE EARNINGS 
INDEXES 

Although we believe that the physician work GPCI is appropriately based on the 
relative earnings of professional workers, the 1990 Census data allow comparisons to relative 
earnings of other occupations. In this chapter, we compare the professional earnings index to 
indexes of earnings of all non-manufacturing workers and of physicians. All non- 
manufacturing workers provides a very broadly based wage comparison to the earnings of 
professionals. A similar broadly-based occupational group is used to update physician fees 
in the Medicare Economic Index. The comparison to physician earnings is relevan^ because 
the GPCI is used to adjust physician fees, and actual physician income is an alternative 
adjustor. Section 5.1 reviews the reasons why we believe that professional earnings is the 
appropriate choice for the physician work GPCI. Comparisons of relative professi -al 
earnings and the earnings of all non-manufacturing workers are presented in Section 5.2. 
Comparisons of relative earnings of physicians and other professionals by Medicare payment 
locality are contained in Section 5.3. 

5.1 Conceptual Issues in the Choice of Physician Work GPCI 

The conceptual justification for use of an index of relative professional earnings as the 
physician work GPCI was developed in Zuckerman, Welch, and Pope (1987), Welch, 
Zuckerman, and Pope (1989), Zuckerman, Welch, and Pope (1990), and Pope, et al (1989). 
Briefly, the physician work GPCI should compensate physicians for cost-of-living and 



gpci\ physrpt\ text 



-28- 

4> 



amenities variations across geographic areas. Since the GPCI is applied to physician fees per 
unit of service, it should be based on earnings per unit time, i.e., hourly earnings. With 
appropriate compensation for cost-of-living and amenities, real physician hourly income will 
be equal everywhere. That is, physicians will not have an incentive to choose one location 
over another. This is both equitable— real net incomes per unit service are equal— and 
efficient-the geographic distribution of physicians is not biased toward any area, given the 
level of demand for physician services. 

Even with an appropriately constructed GPCI, the physician to population ratio will 
vary across areas because of higher demand for physician services in some areas than others. 
For example, lower-income areas may have fewer physicians. If policymakers wish to offset 
the effects of income levels and other factors on the distribution of physicians, they can do so 
through added payments or incentives. For example, physicians who locate in designated 
"shortage areas" can be paid bonuses. However, the basic physician work GPCI should be an 
unbiased measure of the relative hourly earnings necessary to make physicians indifferent 
between locating in different areas. 

Both theory and empirical evidence suggest that wages (earnings) reflect both cost-of- 
living and amenity differences among areas (see the citations in Zuckerman, Welch, and 
Pope, 1987). A cost-of-living index alone is not adequate because it does not reflect amenity 
differences among areas. What hourly earnings should be used in the GPCI? A very 
broadly-based group— say, all workers— is not appropriate because the higher education and 
incorr e level of physician may cause their tastes and cost-of-living to differ from workers as a 
whole. Professional workers are a more narrowly defined group whose education and 
income is more similar to physicians. They are a more appropriate proxy group to use in the 
physician work GPCI. 

Why not use physician income itself in the GPCI? A major conceptual problem with 
using physician income in the GPCI arises from the fact that most physicians are self- 
employed. Thus, physician income has two components: an opportunity wage (cost) and an 
entrepreneurial return, or profit. The opportunity wage is what an employed physician on 
salary could earn, and, as foregone income, is the "cost" of being self-employed. The self- 
employed physician's profit is any net income in excess of the opportunity wage. The GPCI 
should reflect the opportunity wage, but not the profit. The entire Medicare Fee Schedule is 
based on the principle that fees should reflect costs, such as the opportunity wage, but not 
other factors, such as profits. Incorporating profit into the GPCI could create perverse 
incentives. For example, it is not appropriate to pay more per service to physicians in an 



gpci\physrpt\text 



-29- 



area that are especially entrepreneurial and provide more services. Similarly, if physicians in 
an area are increasing their volume of services faster than physicians in other areas, it is not 
appropriate to pay them more per service when the GPC1 is updated. Even the wage of 
employed physicians may not be appropriate for the GPCI. It is influenced by the profit 
opportunities available to self-employed physicians in an area, and is thus not an unbiased 
measure of cost-of-living and amenity differences. 

Using physician income would create a GPCI that is not an independent, exogenous 
measure of the relative cost of physician time. There is general consensus that the historical 
pattern of physician fees was distorted by insurance and other factors. Much influence of 
these historical fee structures persists today. It is not desirable to reflect biased historical 
physician fees and incomes in the GPCI. To be sure, many insurers are currently reforming 
their methods of physician payment. However, even when widespread fee reforms are 
implemented, it would not be appropriate to base the GPCI on physician incomes. Many of 
these reforms are based in whole or part on the Medicare Fee Schedule, including the GPCI. 
Basing the GPCI on the pattern of physician income would therefore create an undesirable 
circularity between physician incomes and the GPCI. An independent, exogenous measure 
of the relative cost of physician time, such as is provided by professional earnings, is needed. 
This principle of exogeneity has also been recognized in the Medicare Economic Index (MEI), 
which is used in updating Medicare physician fees. The appropriate rate of change for the 
physician income component of the MEI is not based on actual physician income, but on the 
earnings of all workers. 

5.2 Comparison of Relative Professional Earnings to Relative Earnings of All 
Non-manufacturing Workers 

Table 5-1 shows Medicare localities with large differences between median hourly 
earnings indexes of all non-manufacturing workers versus the physician work GPCI based on 
professional occupations only. Both quarter variation and full variation results are presented. 
We focus on the full variation results. It should also be noted that the wages of non- 
manufacturing workers are not adjusted for occupation mix differences across areas, whereas 
the professional earnings are adjusted for 1-digit occupation mix. Both indexes use county- 
specific data within large metropolitan areas. 

Non-manufacturing wages tend to be relatively higher in large cities, whereas 
professional earnings are relatively higher in smaller cities and rural areas. For example, in 



gpci\ physrpt\ text 



-30- 



TABLE 5-1 



LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE PHYSICIANS' WORK GPCI AND 
WAGES OF ALL NONMANUFACTURING WORKERS (a) 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


Non- 


CMSA/County 




Non- 


CMSA/County 




Locality Name 


Locality 


Manufact. 


GPCI 


Diff 


Manufact. 


GPCI 


Diff 


Larqest Increases 
















MANHATTAN, NY 


00803/01 


1.146 


1.096 


0.050 


1.585 


1.386 


0.200 


SAN MATEO, CA 


00542/06 


1.094 


1.050 


0.044 


1.378 


1.200 


0.178 


SAN FRANCISCO, CA 


00542/05 


1.111 


1.070 


0.041 


1.443 


1.278 


0.164 


OAKLAND/BERKLEY, CA 


00542/07 


1.081 


1.043 


0.038 


1.323 


1.172 


0.150 


DC +MDA/A SUBURES 


00580/01 


1.093 


1.058 


0.035 


1.372 


1.233 


0.139 


SEATTLE (KING CNTY), WA 


00932/02 


1.041 


1.007 


0.034 


1.164 


1.027 


0.136 


QUEENS, NY 


14330/04 


1.091 


1.059 


0.031 


1.363 


1.237 


0.125 


ALASKA 




1 0Q1 


i (ifii 


n rnn 

U.UjU 


1 ^fi*i 

1 . \J\JyJ 


1 ?46 


120 


URBAN MASS 


00700/01 


1.060 


1.031 


0.029 


1.239 


1.122 


0.117 


MARIN/NAPA/SOLANO, CA 


00542/03 


1.044 


1.016 


0.028 


1.177 


1.066 


0.111 


SW CT 


10230/02 


1.093 


1.067 


0.026 


1.373 


1.268 


0.105 


ATLANTA, GA 


01040/01 


1.032 


1.009 


0.023 


1.128 


1.036 


0.092 


CHICAGO, !L 


00621/16 


1.052 


1.029 


0.023 


1.208 


1.117 


0.091 


S. CNTRL CT 


10230/03 


1.078 


1.055 


0.023 


1.311 


1.220 


0.091 


PH ILLY/PITT MED SHCL/HOSP, PA 


00865/01 


1.053 


1.032 


0.022 


1.214 


1.127 


0.086 


NORTHERN NJ 


00860/01 


1.079 


1.058 


0.021 


1.318 


1.232 


0.085 


SUBURBAN CHICAGO, IL 


00621/15 


1.028 


1.007 


0.021 


1.112 


1.030 


0.083 


SACRAMENTO/SURR. CNTYS, CA 


00542/04 


1.042 


1.021 


0.020 


1.166 


1.085 


0.081 


MASS SUBURBS/RURAL CITIES 


00700/02 


1.040 


1.022 


0.019 


1.162 


1.087 


0.075 


NW AND N. CNTRL CT 


10230/01 


1.066 


1.047 


0.019 


1.262 


1.187 


075 


Larqest Decreases 
















VICTORIA, TX 


00900/32 


0.944 


0.984 


-0.040 


0.775 


0.937 


-0.161 


MC ALLEN, TX 


00900/19 


0.925 


0.962 


-0.037 


0.701 


0.847 


-0.147 


BROWNSVILLE, TX 


00900/10 


0.922 


0.956 


-0.034 


0.690 


0.824 


-0.134 


LAREDO, TX 


00900/33 


0.924 


0.957 


-0.033 


0.696 


0.830 


-0.133 


BEAUMONT, TX 


00900/20 


0.963 


0.994 


-0.031 


0.851 


0.974 


-0.123 


ORANGE, TX 


00900/25 


0.963 


0.994 


-0.031 


0.851 


0.974 


-0.123 


CORPUS CHRISTI, TX 


00900/24 


0.955 


0.984 


-0.029 


0.819 


0.934 


-0.116 


EL PASO, TX 


00900/14 


0.945 


0.973 


-0.028 


0.781 


0.893 


-0.112 


LAFAYETTE, LA 


00528/06 


0.948 


0.975 


-0.027 


0.793 


0.901 


-0.108 


LONGVIEW, TX 


00900/17 


0.948 


0.975 


-0.026 


0.794 


0.899 


-0.106 


DETROIT, Ml 


00710/01 


1.022 


1.047 


-0.025 


1.088 


1.188 


-0.100 



-31- 4 

V 



TABLE 5-1 (continued) 

LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE PHYSICIANS' WORK GPCI AND 
WAGES OF ALL NONMANUFACTURING WORKERS (a) 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


Non- 


CMSA/County 




Non- 


CMSA/County 




Locality Name 


Locality 


Manufact. 


GPCI 


Diff 


Manufact. 


GPCI 


Diff 


MICHIGAN, NOT DETROIT 


00710/02 


0.972 


0.996 


-0.024 


0.887 


0.983 


-0.096 


MIDLAND, TX 


00900/23 


0.982 


1.006 


-0.024 


0.929 


1.025 


-0.096 


MOBILE, AL 


00510/04 


0.952 


0.975 


-0.023 


0.808 


0.902 


-0.094 


LAKE CHARLES, LA 


00528/04 


0.953 


0.975 


-0.022 


0.812 


0.900 


-0.088 


BRAZORIA, TX 


00900/09 


0.972 


0.994 


-0.022 


0.889 


0.975 


-0.086 


REST OF IN 


00630/03 


0.951 


0.973 


-0.022 


0.806 


0.891 


-0.085 


WESTERN TX 


00900/04 


0.937 


0.958 


-0.021 


0.749 


0.834 


-0.085 


LAS VEGAS, ET AL. (CITIES), NV 


01290/01 


0.998 


1.019 


-0.021 


0.992 


1.075 


-0.083 


OSHKOSH (E CNTRL), Wl 


00951/60 


0.952 


0.973 


-0.021 


0.808 


0.891 


-0.083 


REST OF LA 


00528/50 


0.943 


0.963 


-0.019 


0.774 


0.851 


-0.077 


JANESVILLE (S CNTRL), Wl 


00951/54 


0.946 


0.965 


-0.019 


0.784 


0.861 


-0.077 


ALEXANDRIA, LA 


00528/07 


0.940 


0.959 


-0.019 


0.760 


0.836 


-0.076 


NEW ORLEANS, LA 


00528/01 


0.980 


0.999 


-0.019 


0.921 


0.997 


-0.076 


SOUTHWEST Wl 


00951/14 


0.941 


0.960 


-0.019 


0.765 


0.839 


-0.074 


CENTRAL Wl 


00951/13 


0.941 


0.960 


-0.019 


0.765 


0.839 


-0.074 


REST OF KENTUCKY 


00660/03 


0.945 


0.963 


-0.018 


0.778 


0.851 


-0.073 


WACO, TX 


00900/22 


0.948 


0.966 


-0.018 


0.794 


0.866 


-0.072 


REST OF AL 


00510/06 


0.953 


0.970 


-0.018 


0.811 


0.882 


-0.071 



(a) more than a seven percentage point change in the full-variation GPCI 



GPCI: all education sample, one-digit occupations, county data within CMSAs 



-32- 



Manhattan, non-manufacturing wages are 59 percent above the national average whereas 
professional earnings are only 39 percent above the average. Conversely, in Victoria, Texas, 
professional earnings are 94 percent of the national average whereas non-manufacturing 
earnings are only 78 percent of the national average. 

This pattern is probably due in part to the lack of occupation mix adjustments in the 
non-manufacturing wages. Large cities probably have a more skilled, and hence, higher 
wage occupation mix than small cities or rural areas. In addition, the pattern could reflect a 
greater preference of professionals to locate in the larger urban areas. That is, because of the 
non-pecuniary attractions of larger cities for professionals, they may need to be compensated 
less highly for the greater cost-of-living and other disamenities of an urban location. 

The precise reasons for the differences shown in Table 5-1 cannot be uncovered 
without further analysis. However, the comparison does indicate that the use of 
professionals, and the occupation mix adjustment, in the GPCI are having their intended 
effects. A very broad occupational group, such as all non-manufacturing workers, would not 
be appropriate for the GPCI, especially if unadjusted for occupation mix differences among 
areas. Such a physician work GPCI would pay more than necessary to attract professionals, 
such as physicians, to large cities, while not paying enough to attract them to smaller cities 
and rural areas. 

5.3 Comparison of Relative Professional Earnings to Relative Physician Earrrings 
5.3.1 Census Physician Earnings Data 

We obtained two series of physician earnings data from the 1990 Census. The sample 
for the first is all physicians. The sample for the second is physicians cross-classified by the 
Census industry classification of "physicians' offices". The first series provides a larger 
sample size, but includes interns and residents. Since interns and residents earn much less 
than the average for other physicians, earnings of e 1 ! physicians will tend to be lower in areas 
with extensive medical education programs, in particular, large cities. The second series 
excludes interns, residents, and other hospital-based physicians, but at the cost of a smaller 
sample size (see Table 2-1 for the sample size distribution of office-based physicians across 
localities). This series is especially prone to anomalous or inaccurate values in the less 
populous localities due to small sample sizes. Neither series has any controls for physician 
characteristics such as specialty, age, sex, foreign medical graduate, or degree of patient care 
versus administrative or research involvement. Thus, Census-reported physician earnings 



gpd\ physrpt\ text 



may vary across areas because of variations in physician specialty or patient care versus 
research mix. 

We also had series on physician earnings cross-classified by advanced degree status 
and for mean, rather than median hourly earnings. The advanced degree sample provides a 
useful check on status as a medical doctor because occupation is self-reported on the Census. 
The advanced degree sample was similar in size and earnings to the all-education sample. In 
addition, mean earnings patterns for physicians provide a useful check on median earnings 
patterns. Census uses an open-ended category at the highest earnings levels to calculate 
median hourly earnings. Physicians' income is so much greater than average that many 
physicians' hourly incomes must fall into the open-ended range. This could cause 
inaccuracies in the computation of median hourly incomes. However, comparison of mean 
and median physician earnings patterns did not reveal major differences. For our empirical 
comparisons, we used the all-education, median physician earnings series. 

5.3.2 Results 

The correlation between Census physician earnings and the physician work GPCI (full 
variation version) is low. For earnings of all physicians the correlation is .13; for earnings of 
office-based physicians, the correlation is .29. Tables 5-2 and 5-3 presents the comparison of 
professional earnings (the GPCI) and physician earnings indexes for Medicare payment 
localities. The all physicians earnings index is presented in Table 5-2 and the physicians in 
phvsicians' offices index is used in Table 5-3. We focus on the "full variation" columns. 

Perhaps most striking in Table 5-2 is how low physician earnings in several large 
cities are relative to other professional earnings. The index of physician earnings in 
Manhattan, San Francisco, Washington DC, Chicago, Baltimore, urban Massachusetts 
(including Boston and Worcester), and Detroit range from 50 percentage points to 20 
percentage points less than the index of professional earnings. These urban areas are 
characterized by the presence of large teaching hospitals and extensive medical education 
activity. The low physician earnings presumably result, at least in part, from the large 
numbers of lower-earning residents and interns in large urban centers. 

Table 5-3 provides a test of this hypothesis because the physician sample is restricted 
to office-based physicians and should eliminate most interns/ residents. As expected, relative 
earnings of office-based physicians are greater in large cities than relative earnings of all 
physicians. For example, Manhattan's physician earnings index rises from 0.899 to 1.201, and 



gpci\ physrpt\ text 



TABLE 5-2 



LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE PHYSICIANS' WORK GPCI AND 
AND HOURLY EARNINGS OF PHYSICIANS (a) 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


MD-CMSA/ 


CMSA/County 




MD-CMSA/ 


CMSA/County 




Locality Name 


Localitv 


Countv 


GPCI 


Diff 


Countv 


GPCI 


Diff 


Largest Increases 


















SAN ANGELO, TX 


00900/30 


1 


.280 


0.948 


0.331 


2.119 


0.794 


1.325 


ROCKFORD, IL 


00621/02 


1 


.134 


0.981 


0.153 


1.535 


0.924 


0.611 


VICTORIA, TX 


00900/32 


1 


.129 


0.984 


0.145 


1.516 


0.937 


0.579 


AMARILLO, TX 


00900/26 


1 


.117 


0.976 


0.142 


1.470 


0.903 


0.567 


LAKE CHARLES, LA 


00528/04 


1 


.103 


0.975 


0.128 


1.413 


0.900 


0.512 


TYLER, TX 


00900/27 


1 


088 


0.972 


0.116 


1.350 


0.887 


0.464 


WHEELING, WV 


16510/17 


1 


074 


0.959 


0.116 


1.296 


0.834 


0.462 


MONROE, LA 


00528/05 


1 


066 


0.959 


0.107 


1.263 


0.835 


0.428 


KANKAKEE, IL 


00621/06 


1 


060 


0.957 


0.103 


1.240 


0.827 


0.413 


DENTON, TX 


00900/12 


1 


070 


0.969 


0.101 


1.282 


0.876 


0.406 


TEXARKANA, TX 


00900/08 


1 


049 


0.956 


0.093 


1.194 


0.824 


0.370 


BATON ROUGE, LA 


00528/03 


1 


073 


0.985 


0.088 


1.292 


0.940 


0.352 


LAREDO, TX 


00900/03 


1 


043 


0.957 


0.085 


1.171 


0.830 


0.341 


NORTH CENTRAL AL 


00510/02 


1 


056 


0.971 


0.085 


1.223 


0.885 


0.338 


RIVERSIDE, CA 


00542/27 


1 


096 


1.012 


0.084 


1.386 


1.050 


0.336 


ALEXANDRIA, LA 


00528/07 


1 


043 


0.959 


0.084 


1.171 


0.836 


0.335 


LAS VEGAS, ET AL (CITIES), NV 


01290/01 


1 


097 


1.019 


0.079 


1.390 


1.075 


0.315 


NORTHEAST IOWA 


00640/02 


1 


036 


0.959 


0.077 


1.143 


0.834 


0.308 


SM CITIES (CITY LIMITS) KY 


00660/02 


1 


039 


0.963 


0.076 


1.158 


0.854 


0.304 


ABILENE, TX 


00900/29 


1 


034 


0.961 


0.074 


1.137 


0.842 


0.295 


NORTHWEST, IL 


00621/01 


1 


.021 


0.948 


0.072 


1.082 


0.793 


0.290 


QUINCY, IL 


00621/07 


1 


021 


0.948 


0.072 


1.082 


0.793 


0.290 


SOUTHEAST IL 


00621/13 


1 


021 


0.948 


0.072 


1.082 


0.793 


0.290 


SOUTHERN IL 


00621/14 


1 


.021 


0.948 


0.072 


1.082 


0.793 


0.290 


CORPUS CHRISTI, TX 


00900/24 


1 


056 


0.984 


0.072 


1.223 


0.934 


0.289 


NORTHWEST IOWA 


00640/06 


1 


014 


0.944 


0.070 


1.058 


0.776 


0.282 


ORANGE, TX 


00900/25 


1 


062 


0.994 


0.069 


1.249 


0.974 


0.274 


BEAUMONT, TX 


00900/20 


1 


.062 


0.994 


0.069 


1.249 


0.974 


0.274 


MC ALLEN, TX 


00900/19 


1 


.030 


0.962 


0.068 


1.120 


0.847 


0.272 


ARKANSAS 


00520/13 


1 


.015 


0.951 


0.064 


1.060 


0.805 


0.255 


EASTERN VALLEY, WV 


16510/18 


1 


.020 


0.956 


0.063 


1.079 


0.825 


0.254 


REST OF IN 


00630/03 


1 


.036 


0.973 


0.063 


1.142 


0.891 


0.251 


CHAMPAIGN-URBANA, IL 


00621/10 


1 


.015 


0.953 


0.062 


1.060 


0.812 


0.249 


OHIO RIVER VALLEY, WV 


16510/19 


1 


.018 


0.957 


0.061 


1.071 


0.829 


0.243 


VENTURA, CA 


02050/17 


1 


.090 


1.030 


0.061 


1.361 


1.118 


0.243 


DECATUR, CA 


00621/11 


1 


.019 


0.959 


0.060 


1.076 


0.835 


0.241 



TABLE 5-2 (continued) 



LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE PHYSICIANS' WORK GPCI AND 
AND HOURLY EARNINGS OF PHYSICIANS (a) 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


MD-CMSA/ 


CMSA/County 




MD-CMSA/ 


CMSA/County 




Localitv Name 


Localitv 


Countv 


GPCI 


Diff 


Countv 


GPCI 


Diff 


METROPOLITAN IN 


00630/01 


1.049 


0.989 


0.060 


1.196 


0.958 


0.238 


DE KALB, IL 


00621/03 


1.014 


0.956 


0.058 


1.057 


0.823 


0.234 


SALEM, ET AL. (CITIES) OR 


01380/03 


1.024 


0.965 


0.059 


1.094 


0.860 


0.234 


NE RURAL, CA 


00542/02 


1.043 


0.985 


0.058 


1.174 


0.940 


0.234 


OSHKOSH (E CNTRL), Wl 


00951/60 


1 .031 


0.973 


0.058 


1.123 


0.891 


0.232 


EUGENE, ET AL. (CITIES), OR 


01380/02 


1.017 


0.960 


0.057 


1.068 


0.839 


0.229 


REST OF KENTUCKY 


00660/03 


1.018 


0.963 


0.055 


1.072 


0.851 


0.221 


FORT WORTH, TX 


00900/28 


1.043 


0.989 


0.053 


1.171 


0.958 


0.213 


ODESSA, TX 


00900/13 


1.014 


0.961 


0.053 


1.057 


0.844 


0.213 


CHARLESTON, WV 


16510/16 


1.032 


0.979 


0.052 


1.127 


0.917 


0.210 


SMALL GA CITIES 03 


01040/03 


1.021 


0.970 


0.052 


1.085 


0.878 


0.207 


NORTHEAST RURAL TX 


00900/02 


1.015 


0.964 


0.051 


1.060 


0.855 


0.205 


REST OF MISSISSIPPI 


10250/01 


1.004 


0.953 


051 


1.016 


0.811 


0.205 


FORT LAUDERDALE, FL 


00590/03 


1.051 


0.999 


0.051 


1.202 


0.998 


0.204 


S CNTRL IA (EXCL. DES MOINES) 


00640/04 


0.991 


0.941 


0.050 


0.964 


0.764 


0.201 


Largest Decreases 
















MANHATTAN, NY 


00803/01 


0.975 


1.096 


-0.122 


0.899 


1.386 


-0.486 


GALVESTON, TX 


00900/15 


0.875 


0.990 


-0.115 


0.499 


0.961 


-0.462 


SAN FRANCISCO, CA 


00542/05 


0.955 


1.070 


-0.114 


0.822 


1.278 


-0.457 


ALASKA 


01020/01 


0.979 


1 061 


-0.083 


0.914 


1.246 


-0.331 


PHILLY/PITT MED SHCL/HOSP, PA 


00865/01 


0.955 


1.032 


-0.077 


0.821 


1.127 


-0.307 


DC +MDA/A SUBURBS 


00580/01 


0.985 


1.058 


-0.073 


0.940 


1.233 


-0.293 


ELKO & ELY (CITIES), NV 


01290/03 


0.918 


0.987 


-0.069 


0.672 


0.950 


-0.278 


CHICAGO, IL 


00621/16 


0.962 


1.029 


-0.067 


0.850 


1.117 


-0.267 


NYC SUBURBS/LONG 1., NY 


00803/02 


1.003 


1.966 


-0.063 


1.011 


1.263 


-0.252 


BALTIMORE/SURR. CNTYS, MD 


00690/01 


0.965 


1.024 


-0.058 


0.862 


1.095 


-0.233 


URBAN MASS 


00700/01 


0.975 


1.033 


-0.058 


0.900 


1.132 


-0.232 


VERMONT 


00780/50 


0.918 


0.971 


-0.053 


0.671 


0.884 


-0.212 


QUEENS, NY 


14330/04 


1.008 


1.059 


-0.051 


1.032 


1.237 


-0.205. 


SANTA CLARA, CA 


00542/09 


1.014 


1.065 


-0.051 


1.056 


1.261 


-0.205 


DETROIT, Ml 


00710/01 


0.996 


1.047 


-0.051 


0.983 


1.188 


-0.205 



(a) more than a twenty percentage point gain or loss in the full-variation GPCI 

GPCI: all education sample, one-digit occupations, county data within CMSAs (Run #124) 



-36- 



TABLE 5-3 



LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE PHYSICIANS' WORK GPCI AND 
HOURLY EARNINGS OF PHYSICIANS IN PHYSICIANS' OFFICES (a) 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


MD-CMSA/ 


CMSA/County 




■ m Alio * / 

MD-CMSA/ 


CMSA/County 




Locality Name 


Locality 


County 


GPCI 


Dm 


County 


GPCI 


Uln 


Largest increases 
















i_AKEUU, IA 


AAAAA /O O 

00900/33 


4 AAO 

1 .328 


a a c ^ 

0.957 


A OH 

0.371 


A A A A 

2.313 


A O O A 

0.530 


A A Q O 

1 .483 


A M A Dll 1 f\ TV 

AMAKILLU, IX 


aaaaa /ic 
00900/26 


•4 A OO 

1 .183 


A 0"7A 

0.976 


A A A"7 

0.207 


A 70 A 

1 .730 


A AAO 

0.903 


A 007 

0.82/ 


\y a mi/ a i/rr n 

r\ANr\Ar\EE, IL 


00621/06 


1 .144 


A ACT 

0.957 


A A © T 

0.187 


A C "7 A 

1 .576 


A OAT 

0.827 


n 7iO 

0.748 


CAM AMOCI /"» TV 

oAN ANCaELU, I A 


00900/30 


1.110 


A C\ A O 

0.948 


0.161 


4 J AA 

1 .439 


A "7 A A 

0.794 


0.645 


VIL 1 (JKIA, IA 


Anfinn /o a 
00900/32 


a a AA 

1 .1 00 


A AO/I 

0.984 


F\ A A C 

0.1 1 5 


A AAO 

1 .398 


A A07 

0.937 


0.462 


MUNKUE, LA 


AACOO /a c 

00528/05 


A A "7 A 

1 .074 


A ACA 

0.959 


A A A C 

0.1 1 5 


4 AAC 

1 .295 


A OOC 

0.835 


A >i C A 

0.460 


Al CYAMnDIA 1 A 

ALtAANUKIA, LA 


aac aq if\i 
0052O/0 / 


A r\~T A 


A ACA 

0.959 


0.115 


A AAC 

1 .295 


A QOC 

O.806 


A /. CA 

0.459 


TVI CD TV 

1 YLtK, 1 A 


aaaaa 
00900/2 / 


A A "7 y( 

1 .0/4 


A ATA 

0.972 


A A AA 

0.1 02 


A A AC 

1 .295 


A Q Q~7 

0.887 


A A AA 
0.409 


DM 1 (.JIN KUUbt, LA 


00528/0O 


1 .066 


A AO C 

0.985 


A AO A 

0.081 


•1 AC C 

1 .265 


A OA A 

0.940 


A T5C 

O.o25 


1 1 IRPOri/ TY 
LUDBL>Lrv, 1 A 


AAQAA /A i 

00900/21 


A AOC 

1 .005 


A AC C 

0.955 


A AOA 

0.080 


A 4 A A 
1 .141 


A QA4 

0.821 


n ion 
0.020 


r\^JOr\rL'r\U, IL 


nncTi /aa 
00621/02 


A AC A 

1 .060 


A A O A 

0.981 


A A "7 A 

0.079 


A A >4 -1 
1 .241 


A AA/( 

0.924 


O.ol 8 


VA/APO TY 
VVnoU, 1 A 


OOaOO/22 


A A/( 0. 

1 .04 j 


A ACC 

0.966 


A H77 

0.0 / / 


H 4 7A 
1.1/2 


A QCC 

0.866 


n one 
O.OOo 


CM PITIPC fC\TV 1 IMITC\ WV 
OIVI L/l 1 ICO \\>\ 1 T LIMI 1 r\T 


aacc a/aa 
00060/02 


1 .00 / 


A AC O 

0.960 


A A71 
0.0/3 


A A AT 
1.14/ 


A QC.A 

0.854 


A OQ'3 


TFVARKAWA TY 
1 fZ AMr\l\/M\IM, 1 A 


00900/00 


1 .028 


A ACC 

0.956 


A A70 
0.0/2 


•1 A A A 
1.110 


A PA/I 

0.824 


O.ZOo 


1 APAVPTTP 1 A 
LnrnTL 1 it, LA 


aac aq /ac 
00520/06 


•i A» o 

1 .043 


A A "7 C 

0.975 


A AC "7 

0.067 


4 Al A 
1.171 


A AA A 
0.901 


A A7A 

0.2/0 


nCMTflN TY 


00900/12 


1 .035 


A AC A 

0.969 


A AC C 

0.066 


A AAA 

1 .141 


A 07C 

0.8/6 


A ACC 

0.265 


1 PYlMf^TPlM Si 1 rsi IICWII 1 C US/ 
LCAINo 1 VJIN & LUUlo VILLE, r\Y 


00660/01 


4 AC A 

1 .054 


A AOA 

0.989 


A AC /4 

0.064 


A A H C 

1 .215 


A ACO 

0.958 


A ACT 

0.25 / 


\/PMTI IDA A 
VEIN 1 UKA, LA 


A AAC A M *7 
02050/1 7 


1 .092 


A AOA 

1 .030 


A AA O 

0.063 


A OCA 

1 .369 


A AAO 

1.118 


A AC A 

0.251 


MCTDADni ITAM IM 

Mt 1 KVJrULI I AN IN 


00630/01 


1 .049 


0.989 


0.060 


4 A A~7 

1 .197 


A ACO 

0.958 


A A A A 

0.240 


Q A| CM CT Al /f"MTICC\ AD 

oALtM, 1 1 AL. (LI 1 IEb;, UK 


ion /a o 

01 330/03 


a r 

1 .025 


0.965 


0.060 


■a A A O 

1 .098 


A OCA 

0.860 


A AOA 

0.239 


CAM AMTHMIH TV 

oAN AN 1 UNIU, IA 


AAHAA /A "7 

00900/07 


1 .038 


0.979 


0.059 


4 ACA 

1.154 


A A A O 

0.918 


A AOC 

0.236 


MHDTUCACT IA\A/A 

INUK 1 MtAo 1 IUWA 


AAC j r\ /a A 

00640/02 


•4 A 4 A 

1 .016 


0.959 


0.057 


1 .063 


A OO/I 

0.834 


A AAA 

0.229 


KIACJ TLJ ^CMTDAI IAIA/A 

NUK 1 H CENTRAL IOWA 


00640/03 


1 .007 


0.954 


0.054 


1.029 


A A 4 A 

0.814 


A A A C 

0.215 


Ani/A MO A C 

AKKANbAo 


00520/1 3 


1 .004 


0.951 


0.053 


1 .018 


A A A C 

0.805 


A A A O 

0.213 


1 A l/C AUADI CO 1 A 

LAKE CHARLES, LA 


00528/04 


1 .028 


0.975 


0.052 


1.110 


0.900 


A A A A 

0.210 


A nil r— i. | ^ T\y 

ABILENE, TX 


00900/29 


1.012 


0.961 


0.052 


1.048 


0.842 


0.206 


NORTHWEST IOWA 


00640/06 


0.995 


0.944 


0.051 


0.980 


0.776 


0.204 


SMALL GA CITIES 03 


01040/03 


1.020 


0.970 


0.050 


1.080 


0.878 


0.201 


SPRINGFIELD, IL 


00621/09 


1.029 


0.979 


0.050 


1.115 


0.916 


0.199 


CHARLESTON, WV 


16510/16 


1.029 


0.979 


0.050 


1.115 


0.917 


0.198 


SM E. CITIES, MO 


11260/02 


0.990 


0.940 


0.049 


0.959 


0.762 


0.197 


URBAN IN 


00630/02 


1.024 


0.975 


0.049 


1.098 


0.902 


0.196 


REST OF IN 


00630/03 


1.021 


0.973 


0.048 


1.085 


0.891 


0.193 


REST OF KENTUCKY 


00660/03 


1.011 


0.963 


0.048 


1.043 


0.851 


0.192 


CHAMPAIGN-URBANA, IL 


00621/10 


1.000 


0.S53 


0.047 


1.000 


0.812 


0.189 


NORTHWEST, IL 


00621/01 


0.993 


0.948 


0.045 


0.971 


0.793 


0.179 


SOUTHERN, IL 


00621/14 


0.993 


0.948 


0.045 


0.971 


0.793 


0.179 


QUINCY, IL 


00621/07 


0.993 


0.948 


0.045 


0.971 


0.793 


0.179 


SOUTHEAST, IL 


00621/13 


0.993 


0.948 


0.045 


0.971 


0.793 


0.179 



-37- ^\ 



TABLE 5-3 (continued) 



LOCALITIES WITH LARGE DIFFERENCES BETWEEN THE PHYSICIANS' WORK GPCI AND 
HOURLY EARNINGS OF PHYSICIANS IN PHYSICIANS' OFFICES (a) 



QUARTER-VARIATION FULL-VARIATION 





Carrier/ 


MD-CMSA/ 


CMSA/County 




MD-CMSA/ 


CMSA/County 




LULOItly IVCIIIIY7 


LUtdl 1 1 V 


C*f\ 1 1 nt\/ 
vUUI 11 v 




niff 






Diff 

Will 


EASTERN VALLEY, WV 


16510/18 


1.001 


0.956 


0.045 


1.003 


0.825 


0.178 


OHIO RIVER VALLEY WV 


16510/19 

1 V ' 1 W7 1 W 


1 001 


957 


044 


1 006 


8?9 

w . U£ w 


0.177 


CORPUS CHRISTI TX 


00900/24 

W W W W W / «t."T 


1 028 

1 . W <L- W 


984 


044 

W . W*T*T 


1 110 

1 . I t w 


934 

W . W W*T 


0.176 


FORT WORTH TX 


00900/28 

W W W W W/ £— W 


1 033 

1 .WWW 


989 


044 

W . W*T*t 


1 134 

1 . 1 W*T 


958 

w . w w w 


0.176 


LAS VEGAS ET AL (CITIES) NV 


01290/01 

w l t— W \Ji W 1 


1 06? 


1019 

1 , w 1 w 


043 

W . W*T W 


1 ?46 


1 075 

1 . w / w 


0.171 


ORANGE TX 


00900/25 

WW WWW/ <L. W 


1 036 

1 . WWW 


994 

W . W W*T 


043 

W . w*t W 


1 146 

1 . 1 *tw 


974 

W . W 1 "T 


0.171 


BEAUMONT TX 


00900/20 

WW www/ w 


1 036 

1 .www 


* ' . w wH 


w . \JH w 


1 146 


974 

W . W / *T 


0.171 


YUMA, AZ 


01030/08 

W 1 w w w / w w 


1 .028 


985 

w . w w w 


042 

W . W*T-C 


1110 

1 i Mu 


0.941 


0.169 


MILWAUKEE SURBURBS (SE), Wl 


00951/46 


1.030 


0.989 


0.041 


1.119 


0.955 


0.164 


MC ALLEN TX 


00900/19 

WW WWW/ 1 w 


1 002 

1 . W W £- 


96? 

w . W W 


040 


1 007 

1 .ww r 


847 


0.160 


EL PASO, TX 


00900/14 


1 .012 


0.973 


039 

W . W W W 


1 .048 


0.893 


0.155 


Larqest Decreases 
















ALASKA 


01020/01 

W | w*.. W/ W 1 


970 

w . w 1 w 


1 061 

1 . WW 1 


-0 09? 

W .WW.-. 


879 

w . w / w 


1 246 


-0.367 


ELKO & ELY (CITIES') NV 


01290/03 

W 1 WW/ W W 


904 

W . W W*T 


987 


-0 083 


w.w I 1 


950 

w . WWW 


-0 333 

w . w w w 


MIDLAND, TX 


00900/23 

WW W WW/ iL W 


931 

w . w w I 


1 006 


-0 075 


w. / <Cw 


1 025 

1 . W<£- W 


-0.300 


VERMONT 

V La 1 \ 1 W 1 1^1 


00780/50 

WW / WW/ w w 


899 

w . w w w 


971 


-0 07? 


W . WWW 


884 

W . W W*T 


-0 288 

W . £- W W 


BAKERSFIELD CA 


00542/14 


n q^i 

w . w w w 


1 094 


.0 071 


U.O liC 


1 098 

1 . w w<J 


-0 ?86 


GALVESTON, TX 


00900/15 


0.920 


0.990 


-0.071 


0.678 


0.961 


-0.283 


SAN FRANCISCO, CA 


00542/05 


1.000 


1.070 


-0.069 


1.001 


1.278 


-0.277 


RENO, ETAL (CITIES), NV 


01290/02 


0.933 


0.998 


-0.065 


0.731 


0.993 


-0.262 


MARIN/NAPA/SOLANO, CA 


00542/03 


0.962 


1.016 


-0.054 


0.848 


1.066 


-0.218 


OAKLAND/BERKLEY, CA 


00542/07 


0.989 


1.043 


-0.054 


0.955 


1.172 


-0.217 


DC +MD/VA SUBURBS 


00580/01 


1.010 


1.058 


-0.048 


1.041 


1.233 


-0.192 


HAWAII 


01120/01 


0.950 


0.996 


-0.047 


0.799 


0.986 


-0.186 


MANHATTAN, NY 


00803/01 


1.050 


1.096 


-0.046 


1.201 


1.386 


-0.185 


CENTRAL MAINE 


21200/02 


0.916 


0.962 


-0.046 


0.666 


0.847 


-0.181 


NEW HAMPSHIRE 


00780/40 


0.946 


0.990 


-0.045 


0.783 


0.962 


-0.179 


SOUTHERN MAINE 


21200/03 


0.934 


0.976 


-0.043 


0.735 


0.905 


-0.171 


WICHITA FALLS, TX 


00900/34 


0.912 


0.952 


-0.040 


0.647 


0.808 


-0.161 


NYC SUBURBS/LONG 1., NY 


00803/02 


1.028 


1.066 


-0.038 


1.111 


1.263 


-0.152 


FLAGSTAFF, AZ 


01030/05 


0.932 


0.970 


-0.038 


0.730 


0.880 


-0.151 


PRESCOTT, AZ 


01030/07 


0.932 


0.970 


-0.038 


0.730 


0.880 


-0.151 



(a) more than a fifteen percentage point gain or loss in the full-variation GPCI 



GPCI: all education sample, one-digit occupations, county data within CMSAs (Run #123) 

-38- ^ 



San Francisco's index rises from 0.822 to 1.001. Nevertheless, some large cities remain among 
the areas with the lowest physician earnings indexes compared to the other professional 
earnings index (the GPCI), even for office-based physicians. Some other correlates of 
extensive teaching activity in urban centers, such as large numbers of lower-earning research- 
oriented doctors, could help explain the discrepancy. 

Some of the localities with the highest physician earnings indexes relative to the 
professional earnings index appear to be present because of random sampling error. 
Random error because of very small sample sizes of physicians may account for the presence 
of many of the small Texas localities among the highest earning physician areas. Otherwise, 
many of the areas with the largest "excess" physician earnings seem to be smaller cities or 
more rural areas, predominantly in the South and Midwest. It may be that these are areas 
with lower physician to population ratios where physician practices are able to achieve high 
volumes, and thus earn large profits. Also, these small city and rural areas may have less 
managed care and utilization review, further augmenting the scope for profit-enhancing 
entrepreneurial activity by physicians. 

The physician work GPCI may accurately measure the "opportunity wage" component 
of physician earnings in these areas. The discrepancy with the GPCI may arise mostly from 
a higher profit, or entrepreneurial return, component in these areas. As we have argued in 
Section 5.1 above, the profit component of physician net income should not be reflected in 
the physician work GPCI. 

In sum, in many areas there are large disrrepancies between the physician work GPCI 
and relative physician earnings as reported on the 1990 Census. Although many questions 
remain to be answered about geographic differences in physician earnings, we do not believe 
that these discrepancies are evidence of gross inaccuracies in the physician work GPCI. First, 
we believe that the Census data measures the appropriate concept of physician earnings with 
considerable random and systematic error. The random error is due to small sample sizes of 
physicians in many areas. The systematic error is the result of lack of control for physician 
characteristics, such as intern/ resident status, specialty, patient care involvement, etc. More 
importantly, we believe that, for the reasons given in Section 5.1 above, actual physician 
earnings are not an appropriate alternative or comparison for the physician work GPCI. 
Physician net income includes an entrepreneurial, profit component in addition to an 
opportunity wage component. The profit component of earnings should not be reflected in 
the GPCI. Unfortunately, it is difficult to separate the two components of actual physician 
income. 



gpci\ physrpt\ texc 



-39- 



I 



6.0 



FINAL PAYMENT PHYSICIAN WORK GPCIs 



After reviewing the earlier analyses in this report, the Health Care Financing 
Administration (HCFA) made the following decisions: 

• the physician work GPCI should continue to reflect professional earnings 
rather than physician earnings or earnings of all workers; 

• earnings of professionals of all education levels rather than only those with 
advanced degrees should be reflected in the GPCI; 

• a one digit rather than two digit occupation mix adjustment should continue to 
be used; 

• county-specific earnings should be used within large metropolitan areas 
(CMSAs) rather than metropolitan area earnings; and 

• median hourly earnings rather than mean hourly earnings should continue to 
be used. 

In addition, HCFA concurred that the GPCI should be weighted by relative value 
units RVUs (work RVUs in the case of the work GPCI) rather than population. Weighting by 
RVUs has two advantages. First, county RVUs identify the counties where Medicare services 
are provided more accurately than county population. The GPCI should be weighted to 
reflect input prices in the locations where services are performed. Second, weighting by 
RVUs assures that aggregate Medicare Fee Schedule payments for physician work will not 
change if the payment locality configuration is altered (e.g., if a state with multiple localities 
becomes a single statewide locality). The differences between the RVU-weighted work GPCI 
and the population- weighted work GPCI are shown in Appendix 4, where payment localities 
are ranked in descending order of the difference in the two GPCIs. 5 The change in the 
quarter work GPCI from RVU-weighting is one percentage point or less for all payment 
localities. 

The 1996 updated GPCI differs from the 1992 GPCI in the following respects: 



s The weight affects the computation of the GPCI in two places (see Chapter 3). The first is in normalizing hourly earnings 
by national average hourly earnings. For the final work GPCIs, the national average is weighted by work RVUs rather than by 
population. This has the effect of scaling each payment locality's GPCI up or down by the same proportion. The second effect 
occurs in taking a weighted average of county or county part values to form an index value for a payment locality. For the final 
work GPCIs, this average is weighted by work RVUs rather than by population. This change affects each locality's index 
according to the difference in distribution among counties/ county parts of RVUs versus population, and the difference among 
counties/ county parts of hourly professional earnings. 



gpci\physrpt\text 



-40- 



I 



• based on 1990 Census earnings data rather than 1980 Census data; 

• reflects OMB's June 1993 redefinitions of metropolitan areas; 6 

• RVU-weighted rather than population-weighted; 

• reflects earnings of professionals of all education levels rather than 5 or more 
years of college; 

• workers are classified by place of work everywhere rather than place of 
residence outside of large metropolitan areas; 7 and 

• county-specific earnings, rather than metropolitan area earnings, are measured 
within large metropolitan areas (CMS As). 

Of these changes, the use of the more recent 1990 Census earnings data has by far the largest 
overall effect on the work GPCI. 

After the original computation of the 1996 work GPCI, HCFA actuaries determined 
that the quarter work GPCI would have to be rescaled to achieve "budget neutrality", that is, 
to ensure that aggregate payments to physicians are not affected by updating the GPCI. The 
quarter work GPCI was therefore multiplied by the factor 1.00073 and rounded to three 
decimal places, which has the effect of increasing the quarter work GPCI by .001 in each 
payment locality. In addition, a transitional 1995 GPCI was calculated. Congress has 
specified that when the GPCI is updated, in the first year the GPCI will reflect one-half of the 
difference between the current and the updated GPCI. Thus, the 1995 rescaled quarter work 
GPCI was calculated as the simple average of the 1996 rescaled quarter work GPCI and the 
1992 quarter work GPCI. 

Appendix 5 lists: 

1. the 1996 full work GPCI; 

2. the 1996 quarter work GPCI; 

3. the 1996 rescaled quarter work GPCI; 

4. the 1995 rescaled quarter work GPCI; and 

5. the 1992 quarter work GPCI, 

by Medicare payment locality. (The table is sorted by state and locality name within state.) 
In 1995, the 1995 rescaled quarter work GPCI will be used for payment in the Medicare Fee 
Schedule. Beginning in 1996, the 1996 rescaled quarter work GPCI will be used for payment. 



'Although the GPCI is ultimately calculated for Medicare payment localities, the underlying earnings data is tabulated by 
the Census Bureau for metropolitan areas and the nonmetropolitan portions of states before it is crosswalked to localities (see 
Chapter 3). 



In computing the 1992 work GPG with 1980 Census data, for sample size reasons, workers were classified by place of 
residence outside of Consolidated Metropolitan Statistical Areas. This was not necessary with the 1990 Census data. 



gpci\ physrpt\ text 



Appendix 6 ranks Medicare payment localities by descending order of difference 
between the 1996 rescaled quarter work GPCI and the 1992 quarter work GPCI. The largest 
gain is in the Northwest and North Central Connecticut locality, where the the quarter work 
GPCI rises by 4.7 percent. Twelve other localities gain 2 percent or more. The largest 
decrease is in Alaska, whose quarter work GPCI falls by 3.8 percent. Five other localities lose 
more than 3 percent. Since the quarter work GPCI comprises about half of the overall 
Medicare Fee Schedule Geographic Adjustment Factor (GAF), these changes imply less than a 
two and one half percent change in the fee for a typical service in any payment locality due 
to updating the physician work GPCI. 



gpti\physrpt\text 



REFERENCES 



Physician Payment Review Commission, 1991, Annual Report to Congress . Washington DC: 
Physician Payment Review Commission. 

Pope, G., W.P. Welch, S. Zuckerman, and M.G. Henderson, 1989, "Cost of Practice and 
Geographic Variation in Fees," Health Affairs, (8)3:117-128. 

U.S. General Accounting Office, 1993, Medicare Physician Payment: Geographic Adjusters 
A ppropriate But Could be Improved with New Data . GAO Report GAO/HRD-93-93. 

Welch, W. P., S. Zuckerman, and G. Pope, 1989, The Geographic Medicare Economic Index: 
Alternative Approaches . Final Report to the Health Care Financing Administration under 
Grants No. 18-C-98326/1-01, 17-C-99222/3-01, and 17-C-98758/1-03. 

Zuckerman, S., W.P. Welch, and G. Pope, 1987, The Development of an Interim Geographic 
Medicare Economic Index . Report to the Health Care Financing Administration under Grants 
No. 18-C-98326/1-01 and 17-C-98758/1-03. 

Zuckerman, S, W.P. Welch, and G. Pope, 1990, "A Geographic Index of Physician Practice 
Costs", Tournal of Health Economics, (9)1:39-69. 



gpci\physrpt\refs 



APPENDIX 1 



ALTERNATIVE UPDATED PHYSICIAN 
WORK GPCIs COMPARED TO 1992 GPCI 

(Full Variation) 



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CM 


CO 


o 


o 


o 


o 


o 


o 


o 


o 


o 


o 


o 


O 






















CM 


o 


o 


o 


d 


o 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


d 


CD 


CD 


CO 


CD 


CO 


CD 


CD 


CD 


CD 


co 


CD 


CO 


CO 


CD 


CD 


CO 


CD 


CO 




t — 








o 


o 


o 


CM 


CM 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


CO 


LO 


m 


m 




to 


CM 


CM 






"r- 




CD 


CD 


CD 


CO 


CD 


CD 


CD 


CD 


CD 


CO 


CO 


CO 


CD 


CO 


CD 


CO 


CO 


CO 


CO 


CD 


T — 
















































CM 


CM 


CM 



CD 

E 

CO 



re 

o 
o 




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S uj d _i a. 
wiroow 



3 a: z 3 
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co z o to 



CO 
c 
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CO 

CD 

c 

CO 

T3 

CD 



in 
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CO 



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c 



CO 
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cz 

CO 

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CD 



co 

< 

CO 



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CO 
CO 
CD 

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cz 

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a) 

E 

cd 



C 



c 

o 
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CD 

< 

CO 

s 

CD 
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co 
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00 
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c 

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o 

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CO 
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co 
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CO 

cz 

CD 
O 

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Cft 
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C 

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c 

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CO 



cz 

3 

o 
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co 

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co 
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co 
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CD 
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a> 

CD 



co 
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CD 



CO 
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c 

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CO 

CD 

c 

CO 
CD 

E 

CO 
CD 
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3 

Q. 
CD 
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c 
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CD 

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c 

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o 
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o 

0. 

o 

c 

CD 
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CO 

c 

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CD 



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CO 

2 



c 

3 
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o 



c 

3 

o 
O 



CO CO 

o o 



APPENDIX 2 



ALTERNATIVE UPDATED PHYSICIAN 
WORK GPCIs COMPARED TO 1992 GPCI 

(Quarter Variation) 



a 

E 
eg 

CO 

c 

o 

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ca 
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LU 



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cn 


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f—> f — \ / — * I — \ r — N t — \ /"l 1 — \ I — S / — \ / — S /■ — t / — \ I — 1 f— \ / — \ Q Q Q Q Q 



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CN 


CO 


CO 


fo- 


00 


CD 


CS 


o 


CD 


o 


o 


o 


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o 


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CD 


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CO 


co 










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CD 


CD 


CD 


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cn 


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cn 


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00 


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oo 


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CD 


m 


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CD 


r~- 


cn 


cn 


q 


cn 


cn 


cn 


cn 


CD 


cn 


cn 


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cn 


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cn 


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cn 


cn 


cn 


cn 


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cn 


cn 


cn 


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d 


d 


d 


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U">C0CMCDCOCOCOCOCDCDCOCOCOCOtO<DCOCOf""-'*i''«-'<— CD^-COlO 

o>co^cooococo(Ocococooococococococosinii)in^(Ocos 
ocDCDCDCDCDCDCDCDCDCDCDCDO>CDCDCDCDCDCDcncocDCDCDCD 

odddddooddocoodo'ddododbddd 



T-O^NNNNNNNNNNNNNNN010)(DNrtTfMCD 

o^tcocococooocococococooooooDcocosinmininiDiDN 
cncncncDO)cncncncncncncncncncncncncncncncno)cncncncn 

dddddddddddddddddddddddddd 



^•^■scooocooocoooooooeoooeococococosifl^i/imtDiDS 
CDCDOCDCDCDCDCDCDCDCDcncDCDCDCDcncncncncncDCDcncncn 

doT-odoooodooddoodddddddddd 



^(Oin00CO(DCOC0CO00CO(OC0C0COC0CO00N(D'-C0SN0)O 

cotostocococorooococoojoocooooococosTfinin^ifi^s 
cncnocncncncncncncncncncncncncncncncnaf>CDCDcncDcncn 

oo^doododddododododdoddddd 



7J 



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o 



APPENDIX 3 



COMPARISON OF PHYSICIAN WORK GPCI 
TO EARNINGS OF PHYSICIANS AND 
NON-MANUFACTURING WORKERS 

(Full Variation) 



1* 



T-mO>(»T-T-COr-ONCMOP)T-Ttir)nNN(0(»(«)«^0) 
0)T-MOST-SMin00^-N0)(DNmif)S(D^SNm(N00 

oqoooqcqo)oc)NO)cqoqKj s N n n o o? t- t- ^ co n o n o> 

o o o o o o d o d O O O " ' O O O t-' O t-' t-' ^ o 



CD 


o 




ID 






GO 


CD 


CM 


ID 




CD 




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r- 


m 


CO 


o 


CM 


CO 


CD 


CD 


CO 


CD 




CD 


cd 






ID 




o 


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co 


T— 


CD 




CD 


m 




CO 


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m 


o 




m 




CO 


m 


cd 


CD 


CD 


cd 


00 


00 


o 


o 


o 


CM 




CM 




CM 


CD 


CO 


CO 


CO 


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T— 




q 




CD 


d 


d 


d 


d 


d 


d 






d 












d 


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d 








d 






G 





CM 


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CM 






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co 


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CO 




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CD 




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o 


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o 






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CO 






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CM 


CD 




CD 


O 


CD 


CO 


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CD 






CD 




o 


m 


00 




co 




CD 


o 




CD 


CD 


o 


CD 


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APPENDIX 6 

DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN 
WORK GPCI AND 1992 PHYSICIAN WORK GPCI, BY 
MEDICARE PAYMENT LOCALITY, RANKED IN 
DESCENDING ORDER OF DIFFERENCE 



1 



APPENDIX 6 

DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN WORK GPCI AND 1992 PHYSICIAN WORK GPCI, 
BY MEDICARE PAYMENT LOCALITY, RANKED IN DESCENDING ORDER OF DIFFERENCE 



1996 









■9 AA"> A 4 |ai>1 

Kescaiea 


1 99£ 






Carrier 


Locality 




Quarter 


Quarter 




Percent 


Number 


Number 


| ■ ft ft ft. ft 

Locality Name 


Work 


Work 


Chanqe 


Chanae 


10230 


01 


A 1 % A f ft ft 1P\ ft ■ /ft ft fftwaiM g /ft^BB 

NW AND N. CNTRL CT 


1.049 


1.002 


0.047 


4.7 % 


10230 


03 


/ft. IVMI /V» 

S. CNTRL CT 


1.056 


1.018 


0.038 


3.7 


00803 


01 


1 I a L II | ft ftftftHftPi ■ ft I ft |\ * 

MANHATTAN, NY 


1.095 


1.059 


0.036 


3.4 


10230 


04 


EASTERN CT 


1.033 


0.999 


0.034 


3.4 


01040 


01 


ATLANTA, GA 


1.007 


0.975 


0.032 


3.3 


00780 


50 


a ff»=» ft"*, ft |/ft 11 |ft*p 

VERMONT 


0.974 


0.942 


0.032 


3.4 


00542 


05 


SAN FRANCISCO, CA 


1.068 


1.038 


0.030 


2.9 


10490 


01 


RICHMOND 8i CHARLOTTESVILLE, VA 


1.004 


0.975 


0.029 


3.0 


00700 


01 


URBAN MASS 


1.030 


1.002 


0.028 


2.8 


00780 


40 


A ■ 1 1 1 | I A A A ff"ft. /ft ■ II ^ft 

NEW HAMPSHIRE 


0.988 


0.962 


0.026 


2.7 


A A AAA 

21200 


a a 

03 


A> ^ft. 1 I T» 1 | ("ft ft 1 I ■ 1 11 ||— 

SOUTHERN MAINE 


0.980 


0.956 


0.024 


A g* 

2.5 


r\A a a a 

01040 


02 


SMALL GA CITIES 02 


0.981 


A AAA 

0.962 


A A A A 

0.019 


A A 

2.0 


A A OAA 

21200 


A A 

02 


A. 1— A ITH Al AAA 1 A. 1 i™~ 

CENTRAL MAINE 


A A A A 

0.961 


A A A A 

0.942 


a a A A 

0.019 


A A 

2.0 


00860 


f\A 

01 


NORTHERN NJ 


1 .059 


A A A A 

1.040 


A A A A 

0.019 


A O 

1 .8 


00700 


AA 

02 


MASS SUBURBS/RURAL CITIES 


A A A C 

1 .015 


A AA*7 

0.997 


A AH O 

0.018 


A o 

1 .8 


a a a a a 

00900 


31 


AUSTIN, TX 


0.987 


0.969 


A A A O 

0.018 


1 .9 


a a a a a 

21200 


/\ j 

01 


ft l/*M^TI 1 I™ O ft 1 AAA 1 ft i r* 

NORTHERN MAINE 


0.964 


A A A ^ 

0.947 


A f\A ^ 

0.017 


A O 

1 .8 


A A C a a 

00542 


09 


A ft l"^" A A\ 1 A fA A /ft A 

SANTA CLARA, CA 


1.064 


1.048 


A A A A 

0.016 


A C 

1 .5 


00900 


11 


f=ft All A A\ 

DALLAS, TX 


1.012 


0.996 


A A A A 

0.016 


A jO 

1 .6 


A A A A A 

00900 


AO 

28 


f" ART I A / A AX1 1 "TV 

FORT WORTH, TX 


A AO A 

0.989 


A A^O 

0.973 


a e\A a 
0.016 


A C 


AA A AA 

00900 


19 


ft A <■">> All r m ft 1 T\/ 

MC ALLEN, TX 


0.961 


A A A C 

0.945 


A AH C 

0.016 


A "7 

1 .7 


00542 


07 


OAKLAND/BERKLEY, CA 


1.042 


1.028 


0.014 


1.4 


00510 


05 


BIRMINGHAM, AL 


0.994 


0.981 


0.013 


1.3 


00865 


01 


PH ILLY/PITT MED SHCLS/HOSPS, PA 


1.027 


1.014 


0.013 


1.3 


00951 


15 


MADISON (DANE CNTY), Wl 


0.990 


0.977 


0.013 


1.3 


00510 


04 


MOBILE, AL 


0.975 


0.964 


0.011 


1.1 


00542 


06 


SAN MATEO, CA 


1.049 


1.038 


0.011 


1.1 


00740 


05 


KANSAS CITY, KANSAS 


0.989 


0.978 


0.011 


1.1 


00740 


04 


SUBURBAN KANSAS CITY, KANSAS 


0.989 


0.978 


0.011 


1.1 


AA«» A A 

00740 


03 


K.C. (JACKSON CNTY), MO 


0.989 


0.978 


0.011 


A A 

1.1 


AA"7 A A 

00740 


02 


AS 1 / /ft J /ft I ft V y 1 Fft 1 A ■"■"■■^ P™ \ A A /ft. 

N K.C. (CLAY/PLATTE), MO 


0.989 


A A— 7 A 

0.978 


A A A A 

0.01 1 


A A 

1.1 


00870 


A A 

01 


p^i i Ar^r" ■ a ■ Aft ir*\ 

RHODE ISLAND 


A A A A 

1.019 


A AAA 

1.009 


A A A A 

0.010 


A A 
1 .0 


f\A r\Af\ 
Ul U4U 


Ail 

04 


REST OF GA 


A AC C 

0.965 


A ACC 

0.956 


A AAO 

u.uuy 


u.y 


10490 


04 


REST OF VA 


0.976 


0.967 


0.009 


0.9 


00860 


03 


SOUTHERN NJ 


1.024 


1.016 


0.008 


0.8 


00801 


03 


N. CENTRAL CITIES, NY 


1.005 


0.997 


0.008 


0.8 


00803 


02 


NYC SUBURBS/LONG 1., NY 


1.068 


1.060 


0.008 


0.8 


00803 


03 


POUGHKPSIE/N NYC SUBURBS, NY 


1.011 


1.004 


0.007 


0.7 


05440 


35 


TENNESSEE 


0.976 


0.969 


0.007 


0.7 


00900 


24 


CORPUS CHRISTI, TX 


0.983 


0.976 


0.007 


0.7 


00900 


15 


GALVESTON, TX 


0.989 


0.982 


0.007 


0.7 


00900 


18 


HOUSTON, TX 


1.021 


1.014 


0.007 


0.7 


00900 


32 


VICTORIA. TX 


0.983 


0.976 


0.007 


0.7 



gpci\physrpt\APP6.XLS 



2 



APPENDIX 6 

DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN WORK GPCI AND 1992 PHYSICIAN WORK GPCI, 
BY MEDICARE PAYMENT LOCALITY, RANKED IN DESCENDING ORDER OF DIFFERENCE 



1996 









Rescaled 


1992 






Carrier 


Locality 




Quarter 


Quarter 




Percent 


Number 


Number 


Locality Name 


Work 


Work 


Change 


Change 


00510 


01 


NORTHWEST AL 


0.990 


0.985 


0.005 


0.5 


00590 


03 


FORT LAUDERDALE, FL 


0.998 


0.993 


0.005 


0.5 


00590 


01 


REST OF FLORIDA 


0.971 


0.966 


0.005 


0.5 


01040 


03 


SMALL GA CITIES 03 


966 


0.961 


0.005 


0.5 


00650 


01 


REST OF KANSAS 


0.958 


0.953 


0.005 


0.5 


00660 


01 


LEXINGTON & LOUISVILLE, KY 


0.989 


0.984 


0.005 


0.5 


00528 


01 


NEW ORLEANS, LA 


0.999 


0.994 


0.005 


0.5 


00880 


01 


SOUTH CAROLINA 


0.976 


0.971 


0.005 


0.5 


00900 


17 


LONGVIEW, TX 


0.973 


0.968 


0.005 


0.5 


00900 


21 


LUBBOCK, TX 


0.955 


0.950 


0.005 


0.5 


00900 


07 


SAN ANTONIO, TX 


0.978 


0.973 


0.005 


0.5 


02050 


16 


SANTA BARBARA, CA 


1.016 


1.012 


0.004 


0.0 


01380 


01 


PORTLAND, ET AL. (CITIES), OR 


0.997 


0.993 


0.004 


0.4 


00510 


02 


NORTH CENTRAL AL 


0.973 


0.970 


0.003 


0.3 


00542 


03 


MARIN/NAPA/SOLANO, CA 


1.015 


1.012 


0.003 


0.3 


00590 


02 


N/NC FL CITIES 


0.978 


0.975 


0.003 


0.3 


05535 


00 


NORTH CAROLINA 


0.971 


0.968 


0.003 


0.3 


00900 


26 


AMARILLO, TX 


0.975 


C.972 


0.003 


0.3 


10490 


03 


SM TOWN/INDUSTRIAL VA 


0.974 


0.971 


0.003 


0.3 


01030 


99 


REST OF AZ 


0.989 


0.987 


0.002 


0.2 


10230 


02 


SW CT 


1.055 


1.053 


0.002 


0.2 


00740 


01 


ST JOSEPH, MO 


0.952 


0.950 


0.002 


0.2 


00900 


08 


TEXARKANA, TX 


0.955 


0.953 


0.002 


0.2 


01030 


01 


PHOENIX, AZ 


1.004 


1.003 


0.001 


0.1 


01030 


08 


YUMA, AZ 


0.984 


0.983 


0.001 


0.1 


00801 


04 


REST OF NEW YORK 


0.989 


0.988 


0.001 


0.1 


01370 


00 


OKLAHOMA 


0.970 


0.969 


0.001 


0.1 


00973 


20 


PUERTO RICO 


0.883 


0.882 


0.001 


0.1 


10490 


02 


TIDEWATER & N VA CNTYS 


0.990 


0.989 


0.001 


0.1 


00542 


01 


N. COASTAL CNTYS, CA 


1.003 


1.003 


0.000 


0.0 


01290 


03 


ELKO & ELY (CITIES), NV 


0.984 


0.984 


0.000 


0.0 


00621 


12 


EAST ST. LOUIS, IL 


0.988 


0.989 


-0.001 


-0.1 


00528 


04 


LAKE CHARLES, LA 


' 0.974 


0.975 


-0.001 


-0.1 


14330 


04 


QUEENS, NY 


1.058 


1.059 


-0.001 


-0.1 


00865 


03 


SM PA CITIES 


0.983 


0.984 


-0.001 


-0.1 


00900 


06 


TEMPLE, TX 


0.968 


0.969 


-0.001 


-0.1 


00951 


13 


CENTRAL Wl 


0.959 


0.960 


-0.001 


-0.1 


00951 


60 


OSHKOSH (E CNTRL), Wl 


0.973 


0.974 


-0.001 


-0.1 


00951 


14 


SOUTHWEST Wl 


0.959 


0.960 


-0.001 


-0.1 


00510 


03 


SOUTHEAST AL 


0.970 


0.972 


-0.002 


-0.2 


10250 


02 


URBAN MISSISSIPPI 


0.964 


0.966 


-0.002 


-0.2 


00860 


02 


MIDDLE nj 


1.032 


1.034 


-0.002 


-0.2 


16360 


00 


OHIO 


0.991 


0.993 


-0.002 


-0.2 



gpci\physrpt\APP6.XLS 

\o5 



3 



APPENDIX 6 

DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN WORK GPCI AND 1992 PHYSICIAN WORK GPCI, 
BY MEDICARE PAYMENT LOCALITY, RANKED IN DESCENDING ORDER OF DIFFERENCE 



1996 









Repealed 


1992 






farrier 

well 1 Ivl 


L>Uwalliy 




Oi i a rto f 


Onartpr 
vrfudi ici 




Pprrpnt 


IMUIIIUBI 


Kli im har 
IMUIIIUcI 


LUvalllV IMallle 


worn 


UWnrk 
wwiir r 


VrlltilllJB 


wlldllvj" 


oo865 


09 


1 O PA citify 

LO rr\ Vw 1 1 ICO 


1 nnft 

I .UUD 


1 nnR 
I .uuo 


n nn*? 
-u.uuz 


.n 9 


00865 

wwUWw 


04 


Rcej OF PA 


n 07^ 


n 075 

u.y / 3 


_n ono 

~U.UU<C 


-0 9 

-U 


16510 

1 w w 1 W 


18 

1 w 


FASTFRN VAI 1 FY WW 


n QRn 


n Q69 


_A r\r)9 


.0 9 


00740 


06 


RURAI NWOOUNTIFS MO 


n o^n 


n 05^ 


_A AAO 
"U.UUO 


-0 1 

U. w 


00801 


01 

w 1 


RLJFFAI O/SURR PNTYS MY 


1 nn^ 


1 nn6 

I .uuu 


_A ooi 

"U.UUw 


-0 1 
u. w 


16510 

1 WW 1 V/ 


1Q 

1 w 


OHIO RIVFR VAI 1 FY \AA/ 

UnlU rxlVCrx VnLLL I , VVV 


n Q*>Q 


n QRO 


.A 001 
"U.UUw 


-0 1 

U . w 


00951 

wwww 1 




GRPPM RAY iNORTHFASTl Wl 


076 


n 070 


-0 nn^ 

U.UUw 


-0 3 

w . w 


02050 

W«. W WW 


18 

I w 


LOS ANGFI FS MST OF R\ 


1 056 


1 060 

1 .www 


-0 004 

W • WW"T 


-0.4 


02050 


19 


1 OS ANOFI FS f2NR OF 8^ 


1 056 

1 .UJU 


1 060 

1 . www 


-0 004 

w. WW"T 


-0 4 


02050 
u^uwu 


90 


1 OS ANC5FI FS rtRn OF 


I .UjD 


1 n6n 


-0 004 
u.uut 


-0 4 

W . *T 




£ i 


1 OS AMOFI F<5 (ATU OF R\ 
LUO nINoCLCo [ft 1 n Ur 


1 n^R 


1 nftn 

I .uuu 


_A 00/L 

■U.UU*r 


\j .*t 


02050 


99 


1 OS AMOFI PC /RTI-I OF 


I .uoo 


1 n6n 

I .uuu 


_n nn4 

"U.UUH 


-0 4 




d.o 


1 0<5 ANOFI PC /CTM OF H\ 
LUO AINoPLPO (O 1 n Ur O) 


I .UOD 


1 nftn 
I .uuu 


_n ooa 

"U.UUH 




n?n^n 


OA 


1 OC AMPCI CO r7TU OF R\ 
LUO ANotLto \f l n Ur O) 


I .UOD 


1 n^n 

I ,UDU 


_n ooa 

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w .*T 


non^n 


OH 
£.0 


Luo AiNotLto I n Ur o; 


1 AAA 


'\ nftn 
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n nnA 

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ni 1 on 


ni 

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UA\A/A 11/^1 1AM 
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A QQQ 


1 nn^ 

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US 1 ou 


1 1 


C("M ITU inAur^ 
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n oft'i 


n QR7 

u.yo / 


.A AAA 


-0 4 

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nno^i 

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HA 


lAMFCWII 1 F /C PMTDI \ \A/1 
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n oftfi 
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n Q7n 
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1 A ^DACCC AA/ AMTDI \ \A/I 
LA UKUoot (VV OIN 1 KL;, VVI 


A Q70 

u.y / £. 


n Q7R 

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_n nn4 

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.n 4 






dai^cdccici n r*A 
DAI\tKor ItLU , UA 


1 AOI 


1 n9R 
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_A AAC 
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-0 5 

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nn^7n 


ni 


HFI AlA/APC 
DCLAVVAKt 


1 n^i 

I .\J£. I 


1 n9fi 

I ,U4lU 


-0 005 


-0 5 


1 i9An 

1 !ZOU 


ni 

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ct i /"m iic/i n e r-iTiFc mo 
O 1 . LUUIo/Lo t. Ul 1 1 to, MU 


n qr^ 


n QRR 
u.yoo 


-0 005 

"U.UUw 


-0 5 


nnonn 

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on 


RCAI IMAMT TV 
DCAUMUlM 1 , 1 A 


n qq^ 


n QOR 

U.93U 


-0 005 

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w . w 


nnonn 


ID 


rlOAVCAM TV 


n q^q 


n QfiA 
u.yu*r 


_a nn5 

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nnonn 

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ADAMrtC TV 
UKAINoC, 1 A 


n qq > 


n QQR 

U.990 


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nnonn 

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nz 

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\A/P<5TPDM TY 
VV to 1 CKIN 1 A 


n q^r 


n qri 

u.yu i 


_a no 5 

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-0 5 

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nno^i 


I £. 


MADTUIIA/CCT \A/I 
INUK 1 riVVto 1 VVI 


n qri 
u.yo i 


n QRR 
u.yoo 


_A AAt; 
"U.UUW 


-0 5 

w . w 


nn^*?n 


1 o 


ADI/AKICA O 

AKl\AlNoAo 


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n QRn 
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.0 nnR 

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w.w 


UUw 4 ^ 


1 I 


CDCOMA/MAnCDA OA 

rKLolNU/MAUtKA, UA 


1 nnn 
1 .uuu 


1 nnR 
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_n nnR 

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00542 


04 

U*T 


SAPPAMFMTO/SI IRR OMTVC AA 
OMUr\/MVICIN 1 U/OUrxiA. UIN 1 T O, \jr\ 




1 026 


-0 006 

w .www 


-0.6 


non^n 


A 7 


\/CMTI IDA r*A 

VtIN 1 UKA, OA 


1 noR 
I .uzo 


1 01A 


_n nn6 

-u.uuo 


-0 6 

w.w 


nnfi^n 


uo 


Kto 1 Ur IIN 


n 07^ 
u.y r o 


n Q7Q 

u.y / y 


.A AAR 
"U.UUO 


-0 6 

W.W 


nnR^n 

UUOwU 


no 


1 IDDA K I |KI 


A &7A 

u.y / 4 


A QPA 

u.you 


n nnR 

-U.UUO 


-H R 

"w.w 


00690 


01 


BALTIMORE/SURR. CNTYS, MD 


1.021 


1.027 


-0.006 


-0.6 


11260 


03 


REST OF MO 


0.944 


0.950 


-0.006 


-0.6 


01360 


05 


NEW MEXICO 


0.975 


0.981 


-0.006 


-0.6 


00900 


30 


SAN ANGELO, TX 


0.948 


0.954 


-0.006 


-0.6 


00528 


03 


BATON ROUGE, LA 


0.984 


0.991 


-0.007 


-0.7 


00528 


50 


REST OF LA 


0.965 


0.972 


-0.007 


-0.7 


01380 


12 


SW OR CITIES (CITY LIMITS) 


0.967 


0.974 


-0.007 


-0.7 


16510 


16 


CHARLESTON, WV 


0.980 


0.987 


-0.007 


-0.7 


00951 


04 


MILWAUKEE, Wl 


1.001 


1.008 


-0.007 


-0.7 


00580 


01 


DC +MD/VA SUBURBS 


1.051 


1.059 


-0.008 


-0.8 



gpci\physrpt\APP6.XLS 



4 



APPENDIX 6 

DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN WORK GPCI AND 1992 PHYSICIAN WORK GPCI, 
BY MEDICARE PAYMENT LOCALITY, RANKED IN DESCENDING ORDER OF DIFFERENCE 



1996 









Rescaled 


1992 






Carrier 


Locality 




Quarter 


Quarter 




Percent 


Number 


Number 


Locality Name 


Work 


Work 


Chanae 


Chanae 


05130 


12 


NORTH IDAHO 


0.957 


0.965 


-0.008 


-0.8 


00640 


02 


NORTHEAST IOWA 


0.964 


0.972 


-0.008 


-0.8 


00900 


02 


NORTHEAST RURAL TX 


0.960 


0.968 


-0.008 


-0.8 


16510 


20 


SOUTHERN VALLEY, WV 


0.952 


0.960 


-0.008 


-0.8 


01030 


02 


TUCSON, AZ 


0.978 


0.987 


-0.009 


-0.9 


02050 


26 


ANAHEIM/SANTA ANA, CA 


1.037 


1.046 


-0.009 


-0.9 


02050 


28 


SAN DIEGO/IMPERIAL, CA 


1.017 


1.026 


-0.009 


-0.9 


00630 


01 


METROPOLITAN IN 


0.989 


0.998 


-0.009 


-0.9 


00720 


00 


MINNESOTA (BLUE SHIELD) 


0.990 


0.999 


-0.009 


-0.9 


10240 


00 


MINNESOTA (TRAVELERS) 


0.990 


0.999 


-0.009 


-0.9 


00655 


00 


NEBRASKA 


0.951 


0.960 


-0.009 


-0.9 


00801 


02 


ROCHESTER/SURR. CNTYS, NY 


1.012 


1.021 


-0.009 


-0.9 


01380 


02 


EUGENE, ET AL (CITIES), OR 


0.959 


0.968 


-0.009 


-0.9 


01380 


03 


SALEM, ET AL. (CITIES), OR 


0.965 


0.974 


-0.009 


-0.9 


00951 


36 


WAUSAU (N CNTRL), Wl 


0.962 


0.971 


-0.009 


-0.9 


00542 


15 


SAN BERNADINO/E.CTRL CNTYS CA 


1.015 


1.025 


-0.010 


-1.0 


00550 


01 


COLORADO 


0.989 


0.999 


-0.010 


-1.0 


10250 


01 


REST OF MISSISSIPPI 


0.950 


0.960 


-0.010 


-1.0 


00900 


03 


SOUTHEAST RURAL TX 


0.963 


0.973 


-0.010 


-1.0 


00510 


06 


REST OF AL 


0.964 


0.975 


-0.011 


-1.1 


00528 


06 


LAFAYETTE, LA 


0.971 


0.982 


-0.011 


-1.1 


01290 


02 


RENO, ETAL. (CITIES), NV 


0.997 


1.008 


-0.011 


-1.1 


00900 


29 


ABILENE, TX 


0.960 


0.971 


-0.011 


-1.1 


00900 


33 


LAREDO, TX 


0.957 


0.968 


-0.011 


-1.1 


01390 


03 


E CNTRL & NE WA 


0.985 


0.996 


-0.011 


-1.1 


01030 


05 


FLAGSTAFF, AZ 


0.971 


0.983 


-0.012 


-1.2 


01030 


07 


PRESCOTT, AZ 


0.971 


0.983 


-0.012 


-1.2 


00542 


13 


KINGS/TULARE, CA 


0.987 


0.999 


-0.012 


-1.2 


00710 


02 


MICHIGAN, NOT DETROIT 


0.998 


1.010 


-0.012 


-1.2 


00621 


10 


CHAMPAIGN-URBANA, IL 


0.952 


0.965 


-0.013 


-1.3 


00621 


06 


KANKAKEE, IL 


0.959 


0.972 


-0.013 


-1.3 


00621 


15 


SUBURBAN CHICAGO, IL 


1.007 


1.020 


-0.013 


-1.3 


00640 


01 


SOUTHEAST IOWA 


0.963 


0.976 


-0.013 


-1.3 


00900 


27 


TYLER, TX 


0.971 


0.984 


-0.013 


0.0 


01390 


02 


SEATTLE (KING CNTY), WA 


1.006 


1.019 


-0.013 


-1.3 


11260 


02 


SM E. CITIES, MO 


0.940 


0.954 


-0.014 


-1.5 


00820 


01 


NORTH DAKOTA 


0.951 


0.965 


-0.014 


-1.5 


00542 


12 


MONTEREY/SANTA CRUZ, CA 


1.008 


1.023 


-0.015 


-1.5 


00542 


27 


RIVERSIDE, CA 


1.011 


1.026 


-0.015 


-1.5 


00621 


09 


SPRINGFIELD, IL 


0.981 


0.996 


-0.015 


-1.5 


00640 


03 


NORTH CENTRAL IOWA 


0.956 


0.971 


-0.015 


-1.5 


00640 


07 


SOUTHWEST IOWA 


0.953 


0.968 


-0.015 


-1.5 


00751 


01 


MONTANA 


0.952 


0.967 


-0.015 


-1.6 



gpci\physrpt\APP6.XLS 

in1 



APPENDIX 6 



DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN WORK GPCI AND 1992 PHYSICIAN WORK GPCI, 
BY MEDICARE PAYMENT LOCALITY, RANKED IN DESCENDING ORDER OF DIFFERENCE 



1996 











1QQ9 






earner 


uocauiy 




f*\ lift ft $* 


vjcUafTcr 






Number 


Number 


Locality Name 


Work 


work 


Chanqe 


Chanae 


00820 


AO 

02 


0/"M IXLJ HAI/ATA 

oOUTH DAK.O I A 


/> fto ft 

0.936 


A AC H 

0.951 


A f\A C 

-0.015 


-1 .6 


AAAAA 

00900 


22 


WACO, TX 


/> ft ft ft 
0.966 


A AO <4 

0.981 


A f\A C 

-0.015 


4 C 

-1.5 


AAA4 r\ 

00910 


AA 

09 


1 IT A Li 

UTAH 


ft ft^ ft 

0.978 


ft ftft ft 

0.993 


A A 4 C 

-0.015 


4 C 

-1 .5 


AAC A O 

00542 


A A 

10 


MbRCbD/SURR.CNTYS, CA 


4 AAA 

1 .002 


A ft A ft 

1 .018 


ft ft A ft 

-0.016 


A ft 

-1 .6 


00621 


16 


CHICAGO, IL 


A ft ft ft 

1 .028 


1.044 


ft ft A ft 

-0.016 


4 C 

-1.5 


AACCA 

00660 


AO 

02 


OfcA ^ITICC /PITV 1 lfcillTC»\ LAV/ 

SM CI 1 IbS (CITY LIMITS) KY 


A AC A 

0.960 


A A^C 

0.976 


A A 4 fi 

-0.016 


A C 

-1 .6 


00710 


AH 

01 


r^ETDOIT k/ll 

Ub 1 KOI 1 , Ml 


A A J 1 

1 .043 


A AC A 

1 .059 


A AH C 

-0.016 


A C 

-1 .5 


AAftyt A 

UU64U 


AC 

U5 


Uto MvJINto (rULIvvVAKKtN), IA 


A AQA 

u.you 


A Q07 

u.yy / 


A AH 1 
-U.U1 / 


-1 . / 


UUOOU 


U3 


Kto I vJr rvblN I UCr\Y 




A Cx~!A 

u.y /4 


-U.U1 / 


1 7 


U I oOU 


QQ 

yy 




n Qft° 


n Q7Q 

u.y 1 y 


n ni 7 


.1 7 

-i.i 


AAAAA 

uuyuu 


13 


ahccca ty 


u.yyi 


1 .UUo 


n 7 
-U.U I / 




UU542 


no 
Uo 


o 1 OCI\ 1 UN/oUKK. ClN 1 Yo , CA 


i .UU1 


1 .Ul y 


-U.U1 


1 P. 
- 1 .0 


uusyu 


U4 


MIAMI CI 

MIAMI, rl_ 




1 .Uo4 


-U.U 1 


1 7 


A CC< A 

16510 


■I -7 
1 / 


WHbbLINlj, WV 


u.yo/ 


u.y /5 


-U.U! 


' ! .0 


UU542 


AO 

02 


KIC Dl ID A 1 A 

1Mb RURAL, CA 


A QQO 


i .UU1 


-u.uiy 


1 a 

-i .y 


00621 


1 1 


HC^ATI ID II 

DbCATUR, IL 


O.yb^ 


u.yo i 


-u.uiy 


-i .y 


AAAAA 
00900 


O A 

34 


IA/I^LJITA CAI 1 O TV 

WICHITA PALLS, TX 


A QCA 


A OCA 

u.yoy 


-u.ui y 


-2.U 


AAAC A 

00951 


46 


t a 1 1 i a i a | ii/rr o 1 ID| inno VA/I 

MILWAUKbb SUBURBS (Sb), Wl 


A AOA 

o.yyu 


•i r\A a 
1 .Ul U 


A AOA 

-u.uzu 


o n 
-^:.U 


AAOOC 

00525 


21 


WYOMING 


A AftO 

0.968 


A AQQ 

0.988 


A AOA 

-U.U2U 


-2.U 


00640 


ft A 

04 


S CNTRL IA (EXCL. DES MOINES) 


A A A A 

0.941 


0.962 


A A*>H 

-0.021 


o o 
-2.2 


ft ft p ft ft 

00528 


05 


MONROE, LA 


ft ft i- ft 

0.958 


ft ft «7 ft 

0.979 


ft ftft A 

-0.021 


O •! 

-2.1 


AAC >* A 

00640 


AC 

06 


NORTHWEST IOWA 


A t\A "7 
0.94/ 


A AfiA 

0.959 


A AOO 

-U.U22 




ft ft ft ft ft 

00900 


14 


EL PASO, TX 


ft ft — Tft 

0.973 


ft ft ft c 

0.995 


A AOA 

-0.022 


o o 
-2.2 


00621 


01 


NORTHWEST, IL 


ft ft r a 

0.951 


ft ft -7 j 

0.974 


a noo 

-0.023 


O >4 

-2.4 


ft ft ft ft a 

00621 


ft A 

04 


ROCK ISLAND, IL 


ft ft ^ ft 

0.972 


ft ftft c 

0.995 


A AOO 

-0.023 


-2.3 


ft A ft ft ft 

01290 


ft ft 

99 


REST OF NEVADA 


ft ft ft ^ 

0.997 


<i AAA 

1.020 


A AOO 

-0.023 


-2.3 


ft ft ft ft ft 

00690 


02 


WESTERN MD 


ft ft ft ft 

0.982 


A ft ft ft 

1 .006 


A AO A 

-0.024 


O >4 

-2.4 


f\A Ortft 

01290 


ft A 

01 


LAS VEGAS, ET AL. (CITIES), NV 


A ft A ft 

1 .012 


4 AOC 

1 .036 


A AOjI 

-0.024 


o o 
-2.3 


AACO«* 

00621 


AO 

03 


i™\ r— ly AID II 

DE KALB ( IL 


A ACO 

0.953 


ft ft^ ft 

0.978 


A AOC 

-0.025 


O ft 

-2.D 


AAQAA 

uuyuu 


A A 

10 


DDA\A/MC\/II 1 C TV 

BKOWNSVILLt, TX 


A ACC 

0.955 


A AOA 

u.you 


n not 
-U.U25 


o ft 


uuoyu 


AO 

03 


C k /""\l ITU C CUADC 1 in 

SOUTH & c. SHORE MD 


A not 

u.yo5 


i A4 -1 
1 .Ul 1 


a noft 
-U.U2D 


ft 


pi ion 

ui oyu 


AH 

01 


iftl O O'C V A / A /CVAI ec ATTI c\ 

W & SE WA (EXCL SEATTLE) 


AGO 

0.982 


A AAQ 
1 .000 


-U.U2D 


O ft 
-2.D 




AO 
Uf 


Al CVAMnDIA 1 A 

ALbXAlNURIA, LA 


u.yso 


u.yoo 


n n°7 


-9 7 


00621 


08 


NORMAL, IL 


0.969 


0.997 


-0.028 


-2.8 


00621 


07 


QUINCY, IL 


0.946 


0.974 


-0.028 


-2.9 


00621 


13 


SOUTHEAST IL 


0.946 


0.974 


-0.028 


-2.9 


00621 


14 


SOUTHERN IL 


0.946 


0.974 


-0.028 


-2.9 


00900 


12 


DENTON, TX 


0.968 


0.996 


-0.028 


-2.8 


00621 


05 


PEORIA, IL 


0.980 


1.009 


-0.029 


-2.9 


00621 


02 


ROCKFORD, IL 


0.978 


1.010 


-0.032 


-3.2 


00528 


02 


SHREVEPORT, LA 


0.971 


1.003 


-0.032 


-3.2 


00900 


09 


BRAZORIA, TX 


0.993 


1.025 


-0.032 


-3.1 


00900 


23 


MIDLAND, TX 


0.991 


1.023 


-0.032 


-3.1 



gpci\physrpt\APP6.XLS 

.r.<7 



APPENDIX 6 



DIFFERENCE BETWEEN 1996 RESCALED PHYSICIAN WORK GPCI AND 1992 PHYSICIAN WORK GPCI, 
BY MEDICARE PAYMENT LOCALITY, RANKED IN DESCENDING ORDER OF DIFFERENCE 



Carrier Locality 

Number Number Locality Name 



00973 
01020 



50 VIRGIN ISLANDS 
01 ALASKA 



1996 

Rescaled 1992 

Quarter Quarter 

Work Work Change 



Percent 
Change 



0.966 
1.064 



1.000 
1.106 



-0.034 
-0.042 



-3.4 
0.0 



gpci\physrpt\APP6.XLS 



CHS LIBRARY 




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